Tagged articles

Large Language Models

1351 articles · Page 11 of 14

Dec 25, 2024 · Artificial Intelligence

AI-Powered Intelligent Coding: Product Evolution, Technical Advances, and Future Outlook

AI‑powered coding tools—from JetBrains’ free IDEs to VSCode extensions like Cursor and end‑to‑end web platforms—are rapidly evolving, offering code continuation, AI‑driven Q&A, multi‑file editing, and chat interfaces, while advances in context handling, caching, LLM fine‑tuning, and speculative decoding promise faster, more integrated development workflows and a future where IDEs become chat‑centric assistants that streamline debugging, deployment, and junior developer support.

AI codingIDE integrationIntelligent code completion

0 likes · 18 min read

AI-Powered Intelligent Coding: Product Evolution, Technical Advances, and Future Outlook

Architects' Tech Alliance

Dec 23, 2024 · Artificial Intelligence

Why High‑Quality, Massive, Diverse Data Fuels AI Breakthroughs

The article explains how breakthroughs in artificial intelligence depend on high‑quality, large‑scale, and diverse training data, outlines the data‑centric AI movement, details a six‑step workflow for building datasets, and surveys the data industry ecosystem supporting large language model development.

AI dataAnnotationLarge Language Models

0 likes · 7 min read

Why High‑Quality, Massive, Diverse Data Fuels AI Breakthroughs

Fighter's World

Dec 21, 2024 · Artificial Intelligence

Is Pre‑training Coming to an End? Evaluating Data Sufficiency

The article examines Ilya Sutskever’s claim that pre‑training will end, argues that scaling laws still hold and data is not yet a bottleneck, highlights the scarcity of high‑quality frontier data, and explains why the industry is shifting toward inference‑time compute (o1) as a more sustainable path for large language models.

AI trendsInference‑time ComputeLarge Language Models

0 likes · 13 min read

Is Pre‑training Coming to an End? Evaluating Data Sufficiency

Data Thinking Notes

Dec 18, 2024 · Artificial Intelligence

Mastering Prompt Engineering: Advanced Techniques from OpenAI, Anthropic, and Google

This article provides a comprehensive guide to modern prompt engineering, covering foundational principles, detailed techniques such as role‑playing, delimiters, step‑by‑step instructions, and advanced strategies like chain‑of‑thought, reflection, and external tool integration, with real‑world examples from major AI providers and a practical Img2Code case study.

AI best practicesLLM DevelopmentLarge Language Models

0 likes · 24 min read

Mastering Prompt Engineering: Advanced Techniques from OpenAI, Anthropic, and Google

Baidu Geek Talk

Dec 16, 2024 · Artificial Intelligence

AIAPI: Baidu's AI-Native Retrieval System for Large Language Model Applications

AIAPI, Baidu’s AI‑native retrieval platform for large language models, tackles hallucination, slow domain updates, and output opacity by delivering authoritative, timely, full‑content data through a dual‑channel architecture that combines traditional search and RAG, employs reusable ranking, graph‑enhanced data layers, dynamic caching that cuts storage by 70 %, and QueryPlan‑based QoS, achieving markedly higher retrieval quality and a 34 % speed gain with Wenxin 4.0.

AI-Native SystemsAIAPILarge Language Models

0 likes · 12 min read

AIAPI: Baidu's AI-Native Retrieval System for Large Language Model Applications

JD Tech

Dec 14, 2024 · Artificial Intelligence

Generative Retrieval for E‑commerce Search: Lexical and Semantic ID Approaches

This article presents a comprehensive study of generative retrieval for large‑scale e‑commerce search, comparing lexical‑based and Semantic‑ID‑based methods, introducing a Query‑to‑MultiSpan framework, analyzing the sand‑glass distribution problem in residual quantization, and proposing heuristic and adaptive solutions to improve recall and efficiency.

AILarge Language Modelse-commerce search

0 likes · 20 min read

Generative Retrieval for E‑commerce Search: Lexical and Semantic ID Approaches

Alibaba Cloud Big Data AI Platform

Dec 12, 2024 · Artificial Intelligence

How PertEval Reveals the Real Knowledge Limits of Large Language Models

At NeurIPS 2024, Alibaba Cloud's PAI team presented the Spotlight paper PertEval, which introduces knowledge‑invariant perturbations to expose the true knowledge capacity of LLMs, critiques over‑optimistic static benchmarks, and showcases responsible AI solutions and platform demos for enterprise use.

Alibaba CloudLarge Language ModelsNeurIPS 2024

0 likes · 6 min read

How PertEval Reveals the Real Knowledge Limits of Large Language Models

Tencent Tech

Dec 11, 2024 · Artificial Intelligence

Inside Tencent LeYong AI: Solving Enterprise RAG with Knowledge, Engineering & Algorithms

This article explores how Tencent's LeYong AI assistant leverages Retrieval‑Augmented Generation to empower enterprise knowledge retrieval, detailing three capability dimensions—knowledge management, engineering, and algorithmic—along with eight sub‑areas such as knowledge boundaries, quality, permissions, multimodal handling, long‑context span, and complex reasoning.

AI assistantsEnterprise AIKnowledge Management

0 likes · 18 min read

Inside Tencent LeYong AI: Solving Enterprise RAG with Knowledge, Engineering & Algorithms

AntTech

Dec 11, 2024 · Artificial Intelligence

Ant Group’s Selected NeurIPS 2024 Papers: Summaries and Highlights

This article presents a curated overview of fifteen Ant Group research papers accepted at NeurIPS 2024, covering topics such as large language models, knowledge graphs, recommendation systems, privacy-preserving inference, and multimodal learning, with abstracts, paper types, links, and key contributions highlighted.

Ant GroupArtificial IntelligenceLarge Language Models

0 likes · 32 min read

Ant Group’s Selected NeurIPS 2024 Papers: Summaries and Highlights

DevOps

Dec 10, 2024 · Artificial Intelligence

Key Generative AI Trends to Watch in 2024

The article outlines the major 2024 generative AI trends—including realistic expectations, multimodal models, smaller open‑source LLMs, GPU shortages, easier model optimization, custom local pipelines, stronger virtual agents, regulatory and ethical challenges, and the rise of shadow AI—while explaining their technical and business implications.

AI governanceLarge Language Models

0 likes · 17 min read

Key Generative AI Trends to Watch in 2024

AntTech

Dec 10, 2024 · Artificial Intelligence

Three Representative Ant Group Papers at NeurIPS 2024

Ant Group will showcase three flagship papers at NeurIPS 2024—AMOR for adaptable modular knowledge agents, PaRO for efficient data‑parallel training of large language models, and LLMDFA for code data‑flow analysis using LLMs—highlighting novel methods, experimental results, and upcoming live discussions.

Ant GroupArtificial IntelligenceDataflow Analysis

0 likes · 5 min read

Three Representative Ant Group Papers at NeurIPS 2024

AsiaInfo Technology: New Tech Exploration

Dec 9, 2024 · Artificial Intelligence

How Programming Large Models Transform Repository‑Level Code Completion

This article examines how programming large models combined with code knowledge graphs can overcome the limited context of traditional code‑completion tools, detailing key techniques, trigger strategies, context acquisition methods, model fine‑tuning practices, current challenges, and future research directions for intelligent, repository‑wide code suggestions.

AI programmingKnowledge GraphLarge Language Models

0 likes · 14 min read

How Programming Large Models Transform Repository‑Level Code Completion

JD Retail Technology

Dec 9, 2024 · Artificial Intelligence

Generative Retrieval for E‑commerce Search: Lexical‑Based and Semantic‑ID Approaches

This article presents a comprehensive study of generative retrieval in large‑scale e‑commerce search, detailing lexical‑based and SemanticID‑based methods, their challenges such as long‑tail distribution and token length, experimental evaluations, the discovered "sandglass" effect, and proposed solutions to improve recall and efficiency.

AILarge Language Modelse-commerce search

0 likes · 20 min read

Generative Retrieval for E‑commerce Search: Lexical‑Based and Semantic‑ID Approaches

ZhongAn Tech Team

Dec 8, 2024 · Artificial Intelligence

Weekly AI Digest Issue 5: Voice Interaction Trends, End‑to‑End vs. Chain Integration, and Enterprise Solutions

This issue examines the growing importance of voice interaction in AI, highlights Justin Uberti’s move to OpenAI and the launch of GPT‑4o, compares end‑to‑end large‑model and chain‑integration approaches, and offers practical enterprise deployment scenarios for both weak and strong voice‑based interactions.

AIChain IntegrationEnd-to-End

0 likes · 14 min read

Weekly AI Digest Issue 5: Voice Interaction Trends, End‑to‑End vs. Chain Integration, and Enterprise Solutions

Fighter's World

Dec 7, 2024 · Artificial Intelligence

Does Scaling Law Still Hold? Analyzing OpenAI’s 12‑Day Mini Releases and the Future of GPT‑5

The article examines OpenAI’s 12‑day mini‑series, the emergence of o1 and Reinforcement Fine‑Tuning, and uses Epoch AI’s 2024 report to evaluate four critical constraints—power, chip capacity, data scarcity, and latency—that determine whether AI scaling laws can sustain the compute needed for a GPT‑5‑scale model by 2030.

AI scalingData ScarcityLarge Language Models

0 likes · 11 min read

Does Scaling Law Still Hold? Analyzing OpenAI’s 12‑Day Mini Releases and the Future of GPT‑5

Baobao Algorithm Notes

Dec 7, 2024 · Artificial Intelligence

What Is Reinforcement Fine-Tuning (RFT) and How Does It Supercharge LLMs?

Reinforcement Fine-Tuning (RFT) combines supervised fine‑tuning with reinforcement learning to teach large language models to reason more effectively, using separate training and validation datasets, graders, and PPO optimization, and has shown superior performance on tasks like gene prediction and math reasoning compared to standard SFT.

AILarge Language ModelsReinforcement Learning

0 likes · 8 min read

What Is Reinforcement Fine-Tuning (RFT) and How Does It Supercharge LLMs?

NewBeeNLP

Dec 3, 2024 · Artificial Intelligence

Can LLMs Self‑Correct Their Answers? Exploring Reward Models, Loss Functions, and Training Dynamics

The article reflects on open‑source LLMs like Qwen2 and Llama 3.1, questioning whether models should self‑review answers, how hidden states might signal uncertainty, the role of loss‑function design, scaling laws, and the trade‑offs between PPO and DPO in alignment.

Large Language ModelsReward Modelloss function

0 likes · 9 min read

Can LLMs Self‑Correct Their Answers? Exploring Reward Models, Loss Functions, and Training Dynamics

NewBeeNLP

Dec 2, 2024 · Artificial Intelligence

What Are Today’s Unified Generation-and-Understanding Multimodal Model Architectures?

This article surveys current unified generation-and-understanding multimodal large-model architectures, compares LLM-centric and LLM-plus-diffusion designs, extracts common insights, details large-scale training tricks from models like Emu3, Chameleon and Janus, and outlines open research directions for visual encoders.

Large Language ModelsMultimodaldiffusion

0 likes · 5 min read

What Are Today’s Unified Generation-and-Understanding Multimodal Model Architectures?

ZhongAn Tech Team

Dec 1, 2024 · Artificial Intelligence

AI Weekly Digest Issue 4: Market Insights, Industry Solutions, and Emerging Technologies

The fourth AI weekly newsletter reviews recent industry news—including Jensen Huang's robot era vision and Tesla's Optimus plans—introduces Claude's new style‑customization feature, explores AI‑enhanced input methods, and evaluates DeepSeek's R1‑Lite model performance on complex reasoning tasks.

AIAI ApplicationsClaude

0 likes · 10 min read

AI Weekly Digest Issue 4: Market Insights, Industry Solutions, and Emerging Technologies

AntTech

Nov 29, 2024 · Artificial Intelligence

AI Industry Trends in 2024: From Global Slowdown to Chinese Market Acceleration

In 2024, despite a global slowdown in generative AI hype, China's AI market accelerates with rapid application deployments, emerging industries like embodied intelligence and autonomous driving, and a maturing ecosystem that shifts AI from hype to tangible industrial impact.

Artificial IntelligenceChinaIndustry Trends

0 likes · 11 min read

AI Industry Trends in 2024: From Global Slowdown to Chinese Market Acceleration

AI Large Model Application Practice

Nov 29, 2024 · Artificial Intelligence

Understanding RAG: How Retrieval‑Augmented Generation Reduces Large‑Model Hallucinations

This article explains the hallucination problem of large language models, introduces Retrieval‑Augmented Generation (RAG) as a solution, compares RAG with model fine‑tuning, and outlines basic RAG architecture and workflow for practical applications.

Large Language ModelsRAGhallucination mitigation

0 likes · 10 min read

Understanding RAG: How Retrieval‑Augmented Generation Reduces Large‑Model Hallucinations

Ximalaya Technology Team

Nov 29, 2024 · Artificial Intelligence

Applying Large Language Models for AIGC Advertising: Content Generation, Multimodal Understanding, and Creative Optimization at Ximalaya

Ximalaya leverages large language models and AI‑generated content to automate ad creative production, multimodal semantic understanding, and creative selection, slashing image costs to 0.2 CNY, boosting CTR by up to 3.5 %, improving revenue and eCPM by over 2 %, and expanding material diversity fivefold.

AIGCLarge Language Modelscreative optimization

0 likes · 21 min read

Applying Large Language Models for AIGC Advertising: Content Generation, Multimodal Understanding, and Creative Optimization at Ximalaya

Alibaba Cloud Developer

Nov 28, 2024 · Artificial Intelligence

Mooncake: Open-Source KVCache-Centric Architecture Boosting Large-Model Inference

Mooncake, an open-source KVCache-centric inference architecture co-developed by Alibaba Cloud and Tsinghua University's MADSys lab, dramatically improves large-model throughput and reduces cost by decoupling resources, standardizing cache pooling, and integrating with frameworks like vLLM, sparking broad industry interest.

AI infrastructureKVCacheLarge Language Models

0 likes · 4 min read

Mooncake: Open-Source KVCache-Centric Architecture Boosting Large-Model Inference

Kuaishou Large Model

Nov 22, 2024 · Artificial Intelligence

Boost LLM Training on Massive Clusters with DP/TP Overlap and Context Parallelism

This article details a comprehensive set of techniques—including data‑ and tensor‑parallel overlap, context‑parallelism, activation rematerialization, and a performance‑driven cost model—that dramatically improve large‑language‑model training efficiency on ultra‑large GPU clusters while preserving model quality.

Distributed TrainingLarge Language ModelsPerformance Modeling

0 likes · 28 min read

Boost LLM Training on Massive Clusters with DP/TP Overlap and Context Parallelism

HyperAI Super Neural

Nov 20, 2024 · Artificial Intelligence

From Computer Vision to Medical AI: Prof. Xie's Work Hits Nature, NeurIPS, CVPR

Professor Xie's team at Shanghai Jiao Tong University reports rapid progress in AI for Science, detailing multimodal medical AI models, large open datasets, language and vision‑language models, and knowledge‑enhanced representations that outperform existing baselines across multiple benchmarks.

Large Language ModelsMedical AIMultimodal Models

0 likes · 14 min read

From Computer Vision to Medical AI: Prof. Xie's Work Hits Nature, NeurIPS, CVPR

DataFunSummit

Nov 18, 2024 · Artificial Intelligence

Intelligent Data Analysis: Agent Architecture Combined with Semantic Layer for Product Implementation

This article explores how large‑model technologies can address data analysis challenges by introducing an Agent‑based architecture integrated with a semantic layer, detailing design principles, optimization paths, technical implementation, real‑world retail case studies, product design considerations, and future directions for intelligent analytics.

AIAgent ArchitectureBusiness Intelligence

0 likes · 22 min read

Intelligent Data Analysis: Agent Architecture Combined with Semantic Layer for Product Implementation

Alibaba Cloud Developer

Nov 18, 2024 · Artificial Intelligence

Solving Knowledge Challenges in Retrieval‑Augmented Generation: Practical Optimizations

This article shares a half‑year of hands‑on experience with Retrieval‑Augmented Generation, analyzing why simple RAG setups often feel unintelligent, identifying three core knowledge issues, and presenting concrete optimization strategies—including chunking, knowledge expansion, and tag‑based conflict resolution—to improve retrieval and generation performance in low‑resource environments.

AILarge Language ModelsRAG

0 likes · 25 min read

Solving Knowledge Challenges in Retrieval‑Augmented Generation: Practical Optimizations

ZhongAn Tech Team

Nov 16, 2024 · Artificial Intelligence

Weekly AI Digest Issue 2: Video Generation, Large Models, AGI, and LoRA Fine‑Tuning

This weekly AI roundup discusses emerging video generation tools like PixelDance and Vidu 1.5, debates on scaling limits of large models, AGI geopolitical considerations, and a MIT study comparing LoRA with full fine‑tuning for domain adaptation.

AGIAIFine-tuning

0 likes · 8 min read

Weekly AI Digest Issue 2: Video Generation, Large Models, AGI, and LoRA Fine‑Tuning

NewBeeNLP

Nov 14, 2024 · Artificial Intelligence

What’s Trending in Recommendation Systems at KDD 2024? A Comprehensive Paper Overview

The 30th SIGKDD conference in Barcelona featured 2,046 research papers with a 20% acceptance rate, and this article compiles the 59 recommendation‑system papers—covering large‑model recommenders, graph‑based methods, sequential models, fairness, privacy, advertising, debiasing, reinforcement learning and more—for researchers to explore the latest academic advances.

KDD2024Large Language ModelsOnline Advertising

0 likes · 15 min read

What’s Trending in Recommendation Systems at KDD 2024? A Comprehensive Paper Overview

Tencent Docs Tech Team

Nov 13, 2024 · Artificial Intelligence

Technical Architecture and Practices of the AI Document Assistant

This article explores the challenges large language models bring to efficiency tools, outlines the AI document assistant's technical thinking and architecture, and details both application‑side and model‑side practices such as retrieval‑augmented generation, intent recognition, and code‑driven table handling, concluding with key lessons.

AIAI architectureDocument Automation

0 likes · 16 min read

Technical Architecture and Practices of the AI Document Assistant

JD Tech Talk

Nov 11, 2024 · Artificial Intelligence

Prompt Engineering: Concepts, Evolution, Techniques, and a Logistics Application Case

This article explains what Prompt Engineering is, traces its development from early command‑based interactions to modern adaptive and multimodal prompting, details various prompting techniques such as zero‑shot, few‑shot, Chain‑of‑Thought, hallucination‑reduction methods, and demonstrates their practical use in a JD Logistics SKU piece‑type classification case with code examples.

AI promptingChain-of-ThoughtLLM applications

0 likes · 26 min read

Prompt Engineering: Concepts, Evolution, Techniques, and a Logistics Application Case

DataFunSummit

Nov 9, 2024 · Artificial Intelligence

GraphRAG: Using Graph Structures to Enhance Retrieval‑Augmented Generation – Challenges, Methods, and Product Deployments

This article introduces GraphRAG, explains the limitations of traditional RAG, outlines four major challenges (fine‑grained retrieval, global context, similarity vs relevance, and macro‑level reasoning), describes GraphRAG’s graph‑based retrieval strategies, showcases comparative experiments, and presents NebulaGraph’s GenAI Suite and RAG products along with future research directions.

AIGraph DatabasesGraphRAG

0 likes · 16 min read

GraphRAG: Using Graph Structures to Enhance Retrieval‑Augmented Generation – Challenges, Methods, and Product Deployments

Baobao Algorithm Notes

Nov 7, 2024 · Artificial Intelligence

Demystifying FlashAttention: A Minimalist Derivation of the Algorithm

This article presents a concise, step‑by‑step derivation of FlashAttention, explaining the prerequisite linear‑algebra concepts, the softmax simplifications, and the parallel computation workflow—including the LSE‑enhanced version—so readers can grasp the algorithm’s elegance without heavy mathematics.

Algorithm DerivationAttention MechanismFlashAttention

0 likes · 8 min read

Demystifying FlashAttention: A Minimalist Derivation of the Algorithm

NewBeeNLP

Nov 7, 2024 · Artificial Intelligence

Tackling Large Model Hallucinations: Causes, Detection, and Mitigation Strategies

This article provides a comprehensive analysis of large language model hallucinations, detailing their definitions, classifications, root causes, detection techniques, and a wide range of mitigation approaches—including RAG pipelines, decoding strategies, and model‑enhancement methods—to improve reliability and safety in real‑world AI applications.

AI safetyLarge Language ModelsModel Evaluation

0 likes · 22 min read

Tackling Large Model Hallucinations: Causes, Detection, and Mitigation Strategies

DataFunSummit

Nov 6, 2024 · Artificial Intelligence

Applying AIGC to Transform Insurance Marketing at Ant Group

This article explains how Ant Group’s insurance marketing team leverages Artificial Intelligence‑generated content (AIGC) to create personalized marketing materials, automate recommendation workflows, and produce video scripts, thereby improving efficiency, compliance, and user engagement in the insurance sector.

AIGCArtificial IntelligenceInsurance Marketing

0 likes · 9 min read

Applying AIGC to Transform Insurance Marketing at Ant Group

Fighter's World

Nov 1, 2024 · Artificial Intelligence

How Fiercely Competitive Is the Large‑Model Landscape? Insights from the State of AI Report 2024

The State of AI Report 2024 reveals converging capabilities among open and closed LLMs, a shift toward inference compute, benchmark and data contamination challenges, rising synthetic‑data risks, booming robotics research, Nvidia's hardware dominance, and a mix of accurate and missed predictions for the coming year.

AI hardwareAI industryLarge Language Models

0 likes · 15 min read

How Fiercely Competitive Is the Large‑Model Landscape? Insights from the State of AI Report 2024

Infra Learning Club

Oct 31, 2024 · Industry Insights

Top AI Startups to Watch in 2024: 10 Leading and 6 Emerging Companies

The article surveys the most funded and influential AI startups of 2024, profiling ten large‑scale companies such as OpenAI, Anthropic, and Scale AI, and highlighting six promising newcomers, while detailing their products, CEOs, valuations, recent milestones, and industry impact.

2024AI industryAI startups

0 likes · 11 min read

Top AI Startups to Watch in 2024: 10 Leading and 6 Emerging Companies

Infra Learning Club

Oct 31, 2024 · Artificial Intelligence

What Is a Token in Large Language Models?

The article explains that a token is the unit processed by large language models, describes three common tokenizer methods—word‑level, character‑level, and sub‑word level—with English and Chinese examples, discusses their advantages and limitations, and shows how OpenAI’s tokenizer varies across model versions.

Large Language ModelsNLPSubword

0 likes · 5 min read

What Is a Token in Large Language Models?

Smart Era Software Development

Oct 31, 2024 · Artificial Intelligence

How D2LLM and Codefuse‑CGE Are Redefining Search with Large Language Models

The article analyzes D2LLM’s teacher‑student bi‑encoder architecture and Codefuse‑CGE’s PMA‑enhanced code embedding, showing how both models surpass BERT dual encoders and LLM cross‑encoders in accuracy, efficiency, and storage cost across semantic and code search benchmarks.

Bi-EncoderCode EmbeddingLarge Language Models

0 likes · 7 min read

How D2LLM and Codefuse‑CGE Are Redefining Search with Large Language Models

AntTech

Oct 29, 2024 · Artificial Intelligence

Three Ant Group Papers Featured at EMNLP 2024: Dynamic Transformers, Plug‑and‑Play Visual Reasoner, and Efficient Fine‑Tuning of Large Language Models

This announcement introduces three Ant Group papers accepted at EMNLP 2024—Mixture‑of‑Modules for dynamic Transformer assembly, a plug‑and‑play visual reasoning framework built via data synthesis, and a layer‑wise importance‑aware efficient fine‑tuning method for large language models—highlighting their innovations and upcoming live presentations.

AI researchEMNLP 2024Large Language Models

0 likes · 6 min read

Three Ant Group Papers Featured at EMNLP 2024: Dynamic Transformers, Plug‑and‑Play Visual Reasoner, and Efficient Fine‑Tuning of Large Language Models

Alibaba Cloud Infrastructure

Oct 28, 2024 · Artificial Intelligence

How AI Is Redefining the Enterprise CIO Role – Insights from Alibaba Cloud’s CIO

In a detailed interview, Alibaba Cloud’s CIO Jiang Linquan discusses how rapid AI advancements—from large language models to multimodal and reasoning systems—are reshaping CIO responsibilities, accelerating enterprise information system intelligence, and driving new strategies for knowledge bases, customer service, and cross‑departmental adoption.

AICIOCustomer Service

0 likes · 14 min read

How AI Is Redefining the Enterprise CIO Role – Insights from Alibaba Cloud’s CIO

Fighter's World

Oct 26, 2024 · Artificial Intelligence

Key Considerations for Deploying Large Language Models in Cloud Services

The article reflects on Alibaba Cloud's large‑model deployments, outlines four service scenarios, examines three fundamental questions about foundation models, and offers a prioritized roadmap—including prompt engineering, RAG, and organizational changes—to effectively bring LLMs to production.

AI deploymentAlibaba CloudLLMOps

0 likes · 8 min read

Key Considerations for Deploying Large Language Models in Cloud Services

AntTech

Oct 15, 2024 · Artificial Intelligence

AI Large Model Technology Exploration and Application Forum (CNCC2024)

The AI Large Model Technology Exploration and Application Forum, held on October 24‑26, 2024 in Hengdian, Zhejiang, gathers leading experts from Ant Group, universities and research institutes to discuss challenges, knowledge enhancement, data infrastructure, diffusion models, multimodal and medical large models through a series of keynote talks and panel sessions.

AILarge Language Modelsconference

0 likes · 12 min read

AI Large Model Technology Exploration and Application Forum (CNCC2024)

Tencent Advertising Technology

Oct 14, 2024 · Artificial Intelligence

Generative Retrieval Based on Yuan Large Model: Implementation and Practice in Tencent Advertising

This paper presents the implementation and practice of generative retrieval based on Yuan large model in Tencent Advertising, addressing three key challenges: user intent capture, model alignment in advertising domain, and high-performance platform design under ROI constraints.

Large Language ModelsModel OptimizationPrompt engineering

0 likes · 17 min read

Generative Retrieval Based on Yuan Large Model: Implementation and Practice in Tencent Advertising

360 Zhihui Cloud Developer

Oct 11, 2024 · Artificial Intelligence

How 360 Built a Thousand‑GPU AI Supercomputer with Kubernetes and Advanced Scheduling

This article details the design and implementation of 360’s AI Computing Center, covering server selection, network topology, Kubernetes scheduling, training and inference acceleration, and the AI platform’s core, visualization, and fault‑tolerance capabilities for large‑scale AI workloads.

AI infrastructureDistributed TrainingGPU cluster

0 likes · 22 min read

How 360 Built a Thousand‑GPU AI Supercomputer with Kubernetes and Advanced Scheduling

NewBeeNLP

Oct 11, 2024 · Artificial Intelligence

Inside Llama 3: Training, Architecture, and Performance Secrets

An extensive review of Meta’s Llama 3 model breaks down its pre‑training data pipeline, scaling laws, architectural tweaks like GQA and RoPE, post‑training methods such as SFT, DPO, and reward modeling, and evaluates benchmark results, offering practical insights for researchers and engineers building large language models.

BenchmarkingLLaMA-3Large Language Models

0 likes · 32 min read

Inside Llama 3: Training, Architecture, and Performance Secrets

Baobao Algorithm Notes

Oct 10, 2024 · Artificial Intelligence

How MCTS Powers Inference in OpenAI’s o1: A Deep Dive with rStar

This article explains how the inference component of OpenAI’s o1 model can be implemented using Monte‑Carlo Tree Search, detailing the action space, rollout process, UCT scoring, and best‑path selection, with a concrete walkthrough of Microsoft’s open‑source rStar code.

Large Language ModelsMCTSOpenAI o1

0 likes · 26 min read

How MCTS Powers Inference in OpenAI’s o1: A Deep Dive with rStar

Architect

Oct 7, 2024 · Artificial Intelligence

Master Prompt Engineering: A Universal Framework for Building Effective LLM Prompts

This article presents a systematic, four‑part Prompt engineering framework—role definition, problem description, goal setting, and requirement specification—augmented with RAG, few‑shot examples, memory handling, and model‑parameter tuning, enabling developers to craft high‑quality prompts for large language models across diverse tasks.

Large Language ModelsModel ParametersPrompt engineering

0 likes · 28 min read

Master Prompt Engineering: A Universal Framework for Building Effective LLM Prompts

DataFunSummit

Oct 2, 2024 · Artificial Intelligence

NVIDIA’s Solutions for Large Language Models: NeMo Framework, TensorRT‑LLM, and Retrieval‑Augmented Generation

This article explains NVIDIA’s end‑to‑end stack for large language models, covering the NeMo Framework for data processing, training, and deployment, the open‑source TensorRT‑LLM inference accelerator, and the Retrieval‑Augmented Generation (RAG) technique that enriches model outputs with external knowledge.

Large Language ModelsNeMoNvidia

0 likes · 17 min read

NVIDIA’s Solutions for Large Language Models: NeMo Framework, TensorRT‑LLM, and Retrieval‑Augmented Generation

Architect

Sep 28, 2024 · Artificial Intelligence

How Does OpenAI’s o1 Model Leverage Self‑Play RL and New Scaling Laws?

The article provides an in‑depth technical analysis of OpenAI’s multimodal o1 model, explaining its self‑play reinforcement‑learning pipeline, the novel train‑time and test‑time compute scaling laws, its long‑think reasoning abilities demonstrated through a cipher example, and speculative architectures for generator‑verifier systems.

Large Language ModelsOpenAIRL scaling

0 likes · 35 min read

How Does OpenAI’s o1 Model Leverage Self‑Play RL and New Scaling Laws?

Tencent Cloud Developer

Sep 27, 2024 · Artificial Intelligence

A Comprehensive Prompt Engineering Framework: Universal Templates, RAG, Few‑Shot, Memory, and Automated Optimization

The article presents a universal four‑part prompt template—role, problem description, goal, and requirements—augmented with role definitions, RAG‑based knowledge retrieval, few‑shot examples, memory handling, temperature/top‑p tuning, and automated optimization techniques such as APE, APO, and OPRO, enabling developers to reliably craft high‑quality prompts for LLMs.

AI Prompt OptimizationLarge Language ModelsPrompt engineering

0 likes · 26 min read

A Comprehensive Prompt Engineering Framework: Universal Templates, RAG, Few‑Shot, Memory, and Automated Optimization

AntData

Sep 26, 2024 · Artificial Intelligence

DB-GPT: Open-Source AI-Native Data Application Development Framework

DB‑GPT is an open‑source AI‑native data‑application framework that provides multi‑model management, Text‑to‑SQL optimization, RAG, multi‑agent collaboration, and intelligent workflow orchestration, enabling developers to build scalable large‑model database applications, with proven enterprise adoption, community growth, and academic publications.

AIData EngineeringLarge Language Models

0 likes · 6 min read

DB-GPT: Open-Source AI-Native Data Application Development Framework

DataFunTalk

Sep 23, 2024 · Artificial Intelligence

Comprehensive Guide to Selecting, Adapting, and Deploying Large Language Models for Enterprise Applications

This article provides an in‑depth, step‑by‑step guide on how enterprises can choose between open‑source and closed‑source large language models, adapt them through incremental pre‑training, instruction fine‑tuning, and reinforcement learning, and finally deploy them across front‑office, middle‑office, and back‑office scenarios to drive digital transformation.

Enterprise AILarge Language ModelsRLHF

0 likes · 28 min read

Comprehensive Guide to Selecting, Adapting, and Deploying Large Language Models for Enterprise Applications

Refining Core Development Skills

Sep 21, 2024 · Artificial Intelligence

Using GLM-4-Plus Large Model API: Features, Code Samples, and Practical Application Scenarios

This article introduces the rapid rise of large language models, highlights the advantages of the GLM-4-Plus model—including superior language understanding, long‑text handling, and enhanced reasoning—explains how to obtain API credentials, demonstrates request parameters and curl examples, and showcases diverse real‑world use cases such as code generation, social‑media copy, travel planning, and interview question creation.

API usageGLM-4-PlusLarge Language Models

0 likes · 17 min read

Using GLM-4-Plus Large Model API: Features, Code Samples, and Practical Application Scenarios

Kuaishou Tech

Sep 20, 2024 · Artificial Intelligence

Building an LLM-Based Agent Platform for Enterprise Commercialization: Strategies, Architecture, and Practical Insights

This article details the strategic development and technical architecture of SalesCopilot, an LLM-driven agent platform designed for enterprise commercialization, highlighting the implementation of RAG and agent technologies, addressing practical challenges, and sharing key insights for building scalable AI applications.

AI agentsAI evaluationEnterprise AI

0 likes · 15 min read

Building an LLM-Based Agent Platform for Enterprise Commercialization: Strategies, Architecture, and Practical Insights

NewBeeNLP

Sep 20, 2024 · Industry Insights

Why Large Language Models Still Matter: Insights into Industry Trends and Research Directions

The author reflects on the shifting mental state and market sentiment of 2024, noting the waning hype for AI applications, the importance of research over sheer scale, and the evolving role of multimodal LLMs and scientific scaling laws in shaping the future of AI.

AI fundingAI industry trendsLarge Language Models

0 likes · 7 min read

Why Large Language Models Still Matter: Insights into Industry Trends and Research Directions

Baidu Geek Talk

Sep 18, 2024 · Industry Insights

How Baidu’s Large‑Model ‘Yuanji’ Is Transforming Traffic Policing in China

The article examines Baidu Cloud’s large‑model‑powered digital police assistant “Yuanji,” detailing its deployment in Shijiazhuang, its voice‑enabled 24/7 Q&A capabilities, performance metrics, broader city rollouts, and the strategic AI partnership reshaping smart traffic management.

Artificial IntelligenceBaidu CloudDigital Police

0 likes · 9 min read

How Baidu’s Large‑Model ‘Yuanji’ Is Transforming Traffic Policing in China

Huawei Cloud Developer Alliance

Sep 18, 2024 · Artificial Intelligence

How Distributed Training Powers Massive Language Models: Concepts, Strategies, and Code

This article explains why single‑machine resources are insufficient for training ever‑larger language models, introduces the fundamentals of distributed training systems, details various parallel strategies such as data, model, pipeline, and hybrid parallelism, and provides practical PyTorch code and memory‑optimization techniques to accelerate large‑scale model training.

GPULarge Language ModelsPyTorch

0 likes · 29 min read

How Distributed Training Powers Massive Language Models: Concepts, Strategies, and Code

DataFunSummit

Sep 15, 2024 · Artificial Intelligence

AgentUniverse: A Multi‑Agent Framework for Financial Scenarios

This article presents Ant Group's agentUniverse framework, detailing its multi‑agent collaborative mechanisms, architectural design, and real‑world financial applications such as AI assistants, ESG analysis, and automated report generation, while addressing challenges of information‑dense, knowledge‑rich, and decision‑critical finance domains.

AI FrameworkFinancial AILarge Language Models

0 likes · 12 min read

AgentUniverse: A Multi‑Agent Framework for Financial Scenarios

Meituan Technology Team

Sep 12, 2024 · Artificial Intelligence

How BlackPearl Dominated All Three KDD 2024 OAG‑Challenge Tracks with Large‑Model Techniques

The BlackPearl team leveraged large‑model strategies—including iterative self‑refinement, train‑time difficulty increase, test‑time augmentation, grafting‑learning, and boosting—to dominate the WhoIsWho‑IND, PST, and AQA tracks of the KDD 2024 OAG‑Challenge Cup, surpassing traditional feature‑engineered, GNN, and BERT baselines.

AQAAcademic Graph MiningKDD 2024

0 likes · 21 min read

How BlackPearl Dominated All Three KDD 2024 OAG‑Challenge Tracks with Large‑Model Techniques

Baidu Geek Talk

Sep 11, 2024 · Databases

Why Vector Databases Are the Next Big Thing in AI: A Deep Dive into RAG and Baidu’s VectorDB

This article examines the 70‑year evolution of databases, explains how large‑model AI drives the rise of vector databases and Retrieval‑Augmented Generation (RAG), outlines the four‑stage RAG workflow, compares Baidu’s self‑built VectorDB with open‑source alternatives, and showcases real‑world deployments that highlight performance, scalability, and enterprise benefits.

AIDatabase ArchitectureIndustry Insights

0 likes · 16 min read

Why Vector Databases Are the Next Big Thing in AI: A Deep Dive into RAG and Baidu’s VectorDB

DataFunSummit

Sep 5, 2024 · Artificial Intelligence

NVIDIA’s End‑to‑End Solutions for Large Language Models: NeMo Framework, TensorRT‑LLM, and Retrieval‑Augmented Generation

This article introduces NVIDIA’s comprehensive solutions for large language models, covering the NeMo Framework’s full‑stack development pipeline, the open‑source TensorRT‑LLM inference accelerator, and Retrieval‑Augmented Generation techniques, while detailing data preprocessing, distributed training, model fine‑tuning, deployment, and performance optimizations.

Large Language ModelsNeMo FrameworkNvidia

0 likes · 16 min read

NVIDIA’s End‑to‑End Solutions for Large Language Models: NeMo Framework, TensorRT‑LLM, and Retrieval‑Augmented Generation

Baidu Geek Talk

Sep 2, 2024 · Industry Insights

How a R&D Data Platform Leverages Large Language Models to Accelerate Issue Diagnosis

The article explains how the R&D data middle platform integrates large language models to automate data collection, real‑time monitoring, intelligent analysis, and rapid root‑cause identification for online issues, detailing the architecture, wide‑table modeling, generative BI, attribution algorithms, RAG enhancements, and future optimization plans.

Large Language ModelsRetrieval-Augmented Generationdata platform

0 likes · 37 min read

How a R&D Data Platform Leverages Large Language Models to Accelerate Issue Diagnosis

DataFunTalk

Sep 2, 2024 · Artificial Intelligence

Exploring Graph Foundation Models: Concepts, Techniques, and Future Directions

This article introduces graph foundation models, explains their relationship with large language models, reviews recent advances in graph neural networks and representation learning, presents the authors' own research on PT‑HGNN, Specformer and GraphTranslator, and discusses challenges, future research directions, and a Q&A session.

Large Language Modelsfoundation modelsgraph representation learning

0 likes · 23 min read

Exploring Graph Foundation Models: Concepts, Techniques, and Future Directions

NewBeeNLP

Sep 2, 2024 · Artificial Intelligence

Boosting Large Language Model Math Reasoning: Mixed Instructions, Synthetic Data, and Training Optimizations

This article presents a comprehensive technical walkthrough on enhancing large language model mathematical reasoning by reviewing model architectures, introducing mixed CoT‑PoT instructions, generating and filtering synthetic data, and applying multi‑stage training optimizations such as RFT, PPO, and DPO, with detailed experimental results and Q&A insights.

AILarge Language ModelsReward Model

0 likes · 17 min read

Boosting Large Language Model Math Reasoning: Mixed Instructions, Synthetic Data, and Training Optimizations

DataFunTalk

Sep 1, 2024 · Artificial Intelligence

Building Multi‑Scenario AI Assistants with Large Models at Huolala

Huolala, a logistics technology company, shares how it leverages large language models to create personal and office AI assistants across dozens of real‑world scenarios, detailing the underlying platform, prompt engineering, multimodal capabilities, multi‑agent coordination, and the resulting business empowerment.

AI assistantsLarge Language Modelslogistics AI

0 likes · 13 min read

Building Multi‑Scenario AI Assistants with Large Models at Huolala

Baobao Algorithm Notes

Aug 29, 2024 · Artificial Intelligence

Why RLHF Is Essential: The Limits of SFT and the Power of Reward Modeling

The article analyzes why Reinforcement Learning from Human Feedback (RLHF) cannot be replaced by Supervised Fine‑Tuning (SFT), highlighting SFT's lack of negative feedback, its one‑directional attention limitation, and how RLHF's reward models provide crucial safety and performance improvements for large language models.

AI AlignmentLarge Language ModelsRLHF

0 likes · 9 min read

Why RLHF Is Essential: The Limits of SFT and the Power of Reward Modeling

Continuous Delivery 2.0

Aug 29, 2024 · Artificial Intelligence

The Post‑AI Era: How Large Language Models Will Transform Software Development

Matt Welsh argues that despite fifty years of programming advances, humans remain poor at coding, and in the post‑AI era large language models will reshape software engineering, prompting a shift toward prompt engineering, new team roles, and a revival of fundamental engineering practices.

AILarge Language ModelsPrompt engineering

0 likes · 6 min read

The Post‑AI Era: How Large Language Models Will Transform Software Development

Efficient Ops

Aug 28, 2024 · Artificial Intelligence

How Large Language Models Are Revolutionizing Banking Regulatory Interpretation

This article explores how AI-powered large language models enable Chinese commercial banks to automate, accurately match, and predict regulatory requirements, detailing new use‑cases, a prompt‑engineering framework, and the resulting efficiency and risk‑reduction benefits for the financial sector.

AIBankingLarge Language Models

0 likes · 7 min read

How Large Language Models Are Revolutionizing Banking Regulatory Interpretation

AntTech

Aug 28, 2024 · Artificial Intelligence

Ant Group’s Selected Papers at KDD2024: Abstracts and Highlights

The article presents a curated collection of Ant Group's research papers accepted at KDD2024, summarizing each paper's title, type, link, source, relevant fields, and abstract, covering topics such as graph mining, large language models, fraud detection, recommendation systems, and multimodal medical AI.

AI researchAnt GroupKDD2024

0 likes · 31 min read

Ant Group’s Selected Papers at KDD2024: Abstracts and Highlights

ByteDance Data Platform

Aug 27, 2024 · Artificial Intelligence

AI-Driven BI: Achieving Zero-Barrier Data Access and Smart Insights

This article traces the evolution of business intelligence platforms from early report‑centric tools to modern AI‑enhanced, search‑driven solutions, detailing the architectural layers, high‑performance data analysis design, multi‑level aggregation, hot‑cold data tiering, and large‑model applications that enable zero‑threshold data consumption and intelligent insights.

Artificial IntelligenceBusiness IntelligenceLarge Language Models

0 likes · 18 min read

AI-Driven BI: Achieving Zero-Barrier Data Access and Smart Insights

Baidu Geek Talk

Aug 26, 2024 · Artificial Intelligence

RLHF Performance Optimization: PPO Algorithm Acceleration Techniques

The article presents three RLHF‑PPO acceleration techniques—TRT‑LLM‑based text generation speedups, selective activation recomputation with sequence parallelism for dynamic memory reduction, and overlapping pipeline stages for system‑level parallelism—demonstrating a 350 % throughput boost on a 10 B model using 16 A100 GPUs.

Distributed TrainingGPU OptimizationLarge Language Models

0 likes · 16 min read

RLHF Performance Optimization: PPO Algorithm Acceleration Techniques

Java High-Performance Architecture

Aug 25, 2024 · Artificial Intelligence

Can AI Ace the Gaokao Math Test? Surprising Results from Six Top LLMs

A recent evaluation had six leading large‑language‑model products (GPT‑4o, GLM‑4, Wenxin 4.0, Doubao, Baichuan 4, and Qwen‑2.5) answer the first 14 objective questions of the new Gaokao mathematics I paper, revealing that only GLM‑4 surpassed the 60% passing threshold while the others performed far below expectations.

AIGLM-4Gaokao

0 likes · 7 min read

Can AI Ace the Gaokao Math Test? Surprising Results from Six Top LLMs

21CTO

Aug 24, 2024 · Artificial Intelligence

How GenAI Is Revolutionizing Legacy Code Reverse‑Engineering and Developer Productivity

This article explores how generative AI tools like ChatGPT, GitHub Copilot, and Amazon Q are enabling developers to rapidly reverse‑engineer, document, and modernize legacy codebases, reduce technical debt, and transform software development practices worldwide.

AI ToolsGenAILarge Language Models

0 likes · 12 min read

How GenAI Is Revolutionizing Legacy Code Reverse‑Engineering and Developer Productivity

DataFunTalk

Aug 24, 2024 · Artificial Intelligence

Improving the Mathematical Reasoning Ability of Large Language Models: Overview, Mixed Instructions, Synthetic Data, and Training Optimization

This article presents a comprehensive approach to enhancing large language models' mathematical reasoning by reviewing model architectures, introducing mixed CoT‑PoT instructions, generating and filtering synthetic data, and applying multi‑stage training optimizations such as RFT, PPO, and DPO, with detailed experimental results and Q&A.

AILarge Language ModelsReward Model

0 likes · 16 min read

Improving the Mathematical Reasoning Ability of Large Language Models: Overview, Mixed Instructions, Synthetic Data, and Training Optimization

DataFunSummit

Aug 23, 2024 · Artificial Intelligence

Applying Large Language Models to Automotive Industrialization: Practices and Experiences

This presentation outlines the development of ChatGPT, the underlying principles of large language models, and how they empower new industrialization in automotive manufacturing, detailing practical implementations, agent architectures, data and model closed loops, and case studies such as intelligent inspection and G8D agents.

Agent ArchitectureAutomotiveChatGPT

0 likes · 13 min read

Applying Large Language Models to Automotive Industrialization: Practices and Experiences

Alibaba Cloud Developer

Aug 23, 2024 · Artificial Intelligence

Mastering Prompt Engineering: Advanced Techniques from Top AI Labs

This comprehensive guide examines cutting‑edge prompt‑engineering strategies—covering clear instruction design, role‑playing, separators, step‑by‑step workflows, external tools, systematic testing, and case studies from Anthropic, Google, and practical Img2Code applications—to help developers achieve more accurate and powerful interactions with large language models.

AI developmentLarge Language ModelsModel Evaluation

0 likes · 21 min read

Mastering Prompt Engineering: Advanced Techniques from Top AI Labs

Huolala Tech

Aug 22, 2024 · Artificial Intelligence

How Large Language Models Automate Order Cancellation Responsibility at HuoLala

This article explains how HuoLala leverages large language models, multimodal feature integration, and retrieval‑augmented generation to automatically determine responsibility for order cancellations, improving accuracy, explainability, and driver‑user experience.

AILarge Language ModelsRAG

0 likes · 10 min read

How Large Language Models Automate Order Cancellation Responsibility at HuoLala

DaTaobao Tech

Aug 21, 2024 · Artificial Intelligence

Mastering Custom Large‑Model Training: Data Strategies, LoRA Tricks, and Resource Planning

This article provides a comprehensive, step‑by‑step guide to training customized large language models, covering industry‑specific needs, data privacy, meticulous data cleaning, optimal data‑ratio balancing, token budgeting, GPU memory accounting, LoRA fine‑tuning techniques, and practical evaluation metrics for robust AI deployment.

AI trainingFine-tuningGPU memory

0 likes · 23 min read

Mastering Custom Large‑Model Training: Data Strategies, LoRA Tricks, and Resource Planning

Ops Development Stories

Aug 21, 2024 · Operations

How Large Language Models Can Transform Ops Fault Handling: A Practical Guide

This article outlines a typical operations incident workflow, identifies four key stages where large language models can assist, discusses implementation challenges, introduces the Ops framework and Copilot design, and shares practical examples and a real‑world case to help engineers adopt AI‑driven fault management.

AI opsAutomationIncident Management

0 likes · 19 min read

How Large Language Models Can Transform Ops Fault Handling: A Practical Guide

Ops Development & AI Practice

Aug 16, 2024 · Industry Insights

How LLMs Are Evolving from Language Mimicry to Real-World Simulation

Recent breakthroughs in AI, from large language models gaining real-world simulation abilities to rapid AI-chip advancements and the surge of open-source models, are reshaping industries, highlighting both unprecedented opportunities and the need for ethical, secure deployment across sectors.

AI chipsArtificial IntelligenceLarge Language Models

0 likes · 7 min read

How LLMs Are Evolving from Language Mimicry to Real-World Simulation

DaTaobao Tech

Aug 16, 2024 · Artificial Intelligence

Effective Prompt Design for Large Language Models

Effective prompt design for large language models requires clear goals, relevant context, explicit input/output formats, evaluation criteria, and illustrative examples, combined with specific language, step‑by‑step instructions, edge‑case handling, ethical considerations, and proper tokenization, encoding, decoding, and post‑processing to produce accurate, concise, low‑hallucination responses.

AILarge Language ModelsPrompt design

0 likes · 33 min read

Effective Prompt Design for Large Language Models

DataFunSummit

Aug 16, 2024 · Artificial Intelligence

Educational Large Language Model Research and Product Applications for Youth Programming

The presentation outlines the challenges of sparse data and delayed learning effects in youth programming education, introduces three technical breakthroughs—dual‑data model training, hierarchical knowledge‑graph prompting, and reinforcement‑based cognitive recommendation—and showcases product implementations such as the Frog Programming Platform, AI learning machine, and digital‑human recorded courses.

AILarge Language Modelseducation

0 likes · 19 min read

Educational Large Language Model Research and Product Applications for Youth Programming

Qunhe Technology Quality Tech

Aug 14, 2024 · Artificial Intelligence

Should Your Testing Team Build a Private LLM or Use RAG with a General Model?

This article compares the high costs and technical challenges of building a private large language model with the benefits, flexibility, and lower risk of using Retrieval‑Augmented Generation (RAG) on a general LLM, offering practical guidance for testing teams seeking AI assistance.

AILarge Language ModelsModel deployment

0 likes · 11 min read

Should Your Testing Team Build a Private LLM or Use RAG with a General Model?

AntTech

Aug 13, 2024 · Artificial Intelligence

Ant Group Contributions to ACL 2024: Summaries of 14 Accepted Papers Across NLP and AI

From August 11‑16, 2024 the ACL conference in Bangkok featured 14 Ant Group papers covering large‑scale information extraction, decomposed LLMs for semantic search, multimodal hallucination detection, long‑context attention mechanisms, concept‑reasoning datasets, knowledge‑graph alignment, and more, highlighting the group's breadth in natural language processing and AI research.

ACL2024Large Language ModelsMultimodal

0 likes · 20 min read

Ant Group Contributions to ACL 2024: Summaries of 14 Accepted Papers Across NLP and AI

DaTaobao Tech

Aug 12, 2024 · Artificial Intelligence

Challenges and Optimization Techniques for Retrieval‑Augmented Generation (RAG)

Deploying large language models faces domain gaps, hallucinations, and high barriers, so Retrieval‑Augmented Generation (RAG) combines retrieval with generation, and advanced optimizations—such as RAPTOR’s hierarchical clustering, Self‑RAG’s self‑reflective retrieval, CRAG’s corrective evaluator, proposition‑level Dense X Retrieval, sophisticated chunking, query rewriting, and hybrid sparse‑dense methods—are essential for improving accuracy, reducing hallucinations, and achieving efficient, scalable performance.

AILarge Language ModelsOptimization

0 likes · 22 min read

Challenges and Optimization Techniques for Retrieval‑Augmented Generation (RAG)

DataFunSummit

Aug 8, 2024 · Artificial Intelligence

Exploring Training and Alignment Techniques for Financial Large Models

The announcement details a DataFun Summit 2024 session where Du Xiaoman AI researcher Huo Liangyu will present on the challenges, development, and alignment methods of the Xuan Yuan financial large language model, highlighting RLHF techniques, data collection, and real‑world deployment insights for the finance sector.

AIFinancial AILarge Language Models

0 likes · 6 min read

Exploring Training and Alignment Techniques for Financial Large Models

Data Thinking Notes

Aug 6, 2024 · Artificial Intelligence

How Large Language Models Are Revolutionizing R&D Operations and Telecom Networks

Large language models are increasingly applied in research and development operations, boosting efficiency and automating processes such as coding assistance, testing, requirement analysis, documentation, knowledge management, and network traffic analysis, while also enhancing security and enabling intelligent transformation across industries, especially telecom.

AI in telecomLarge Language ModelsR&D automation

0 likes · 3 min read

How Large Language Models Are Revolutionizing R&D Operations and Telecom Networks

DeWu Technology

Aug 5, 2024 · Frontend Development

Large Model Innovations Redefining Frontend Development – Key Takeaways

The July 14 DeWu tech salon showcased how large language models are reshaping frontend development, featuring insights from NetEase, Alibaba, and DeWu experts on AI‑driven low‑code platforms, intelligent coding assistants, and practical implementation strategies, with over 20,000 online viewers.

AIFrontendLarge Language Models

0 likes · 8 min read

Large Model Innovations Redefining Frontend Development – Key Takeaways

Software Development Quality

Aug 5, 2024 · Artificial Intelligence

How Large Language Models Can Transform Software Testing

This article explores how large language models can automate test case generation, predict defects, analyze results, optimize strategies, execute intelligent testing, and assist compatibility checks, while providing practical tools, real-world case studies, and a step‑by‑step GPT‑4 testing workflow.

AI testingLarge Language ModelsSoftware testing

0 likes · 15 min read

How Large Language Models Can Transform Software Testing

DataFunSummit

Aug 4, 2024 · Artificial Intelligence

Graph Technology Overview and Applications – From GraphGPT to Graph Databases

This article presents a comprehensive overview of recent advances in graph technology, covering GraphGPT for large language models, knowledge transfer on complex graphs, financial fraud detection, telecom network optimization, graph foundation models, Baidu's multi‑domain recommendation, high‑availability graph databases, and Kuaishou's efficient recommendation architecture.

Graph DatabasesLarge Language ModelsRecommendation Systems

0 likes · 4 min read

Graph Technology Overview and Applications – From GraphGPT to Graph Databases

NewBeeNLP

Aug 3, 2024 · Artificial Intelligence

Extending LLM Context to 1M Tokens: SAMBA, CoPE, RoPE, Retrieval Heads & Infini‑Attention

This article reviews recent research on extending large language model context windows to millions of tokens, covering SAMBA's hybrid architecture, Contextual Position Encoding (CoPE), RoPE base length theory, Retrieval Head analysis, and the memory‑efficient Infini‑Attention mechanism.

LLM researchLarge Language Modelsefficient attention

0 likes · 10 min read

Extending LLM Context to 1M Tokens: SAMBA, CoPE, RoPE, Retrieval Heads & Infini‑Attention

Data Thinking Notes

Aug 1, 2024 · Artificial Intelligence

Unlocking Vertical Domain LLMs: Advantages, Challenges, and Alignment Strategies

Over the past year our team explored applying large language models to specialized domains, detailing their professional benefits, unique challenges such as accuracy and knowledge‑base maintenance, and presenting solutions like alignment enhancement via BPO, Text2API, RAG, and advanced SFT/DPO techniques.

Large Language ModelsModel AlignmentRAG

0 likes · 10 min read

Unlocking Vertical Domain LLMs: Advantages, Challenges, and Alignment Strategies

Xiaohongshu Tech REDtech

Aug 1, 2024 · Artificial Intelligence

Xiaohongshu Search Advertising Recall: Practices, Metrics, and Large‑Model Integration

Xiaohongshu’s search advertising recall system evolves from keyword bidding to BERT‑based vector retrieval and LLM‑enhanced query rewriting, using dual semantic and efficiency models, water‑level metrics, and GPU‑accelerated engineering to achieve 80 % click coverage, 60 % conversion coverage and a 5 % CPM lift.

Artificial IntelligenceLarge Language Modelsefficiency optimization

0 likes · 33 min read

Xiaohongshu Search Advertising Recall: Practices, Metrics, and Large‑Model Integration

Sohu Tech Products

Jul 31, 2024 · Artificial Intelligence

MMEvalPro: A Trustworthy Benchmark for Evaluating Multimodal Large Models

MMEvalPro, a new benchmark created by researchers from Peking University, Chinese Academy of Medical Sciences, CUHK and Alibaba, augments existing multimodal datasets with perception and knowledge questions and introduces a Genuine Accuracy metric, revealing that top multimodal models still lag far behind humans and exposing shortcut‑driven performance on prior tests.

Large Language ModelsMMEvalProMultimodal Evaluation

0 likes · 11 min read

MMEvalPro: A Trustworthy Benchmark for Evaluating Multimodal Large Models

NewBeeNLP

Jul 31, 2024 · Artificial Intelligence

Training 7B–13B LLMs: Practical Tips, Hyperparameters, and Scaling Challenges

The article shares hands‑on experience training 7‑ and 13‑billion‑parameter language models, covering essential hyper‑parameters, hardware requirements, data quality considerations, open dataset resources, and the systemic difficulties that arise when scaling to trillion‑parameter models.

LLM trainingLarge Language Modelshyperparameters

0 likes · 8 min read

Training 7B–13B LLMs: Practical Tips, Hyperparameters, and Scaling Challenges

Tencent Cloud Developer

Jul 30, 2024 · Artificial Intelligence

A Systematic Guide to Prompt Engineering: From Zero to One

This guide walks readers from beginner to proficient Prompt Engineer by outlining the evolution of prompting, introducing a universal four‑component template, and detailing a five‑step workflow—including refinement, retrieval‑augmented generation, chain‑of‑thought reasoning, and advanced tuning techniques—plus evaluation metrics for LLM performance.

AI promptingChain-of-ThoughtLLM Optimization

0 likes · 51 min read

A Systematic Guide to Prompt Engineering: From Zero to One

21CTO

Jul 28, 2024 · Artificial Intelligence

How Anaconda Is Building an AI Operating System with High‑Performance Python

At PyCon US 2024, Anaconda’s Peter Wang outlined the company’s strategy to create an AI operating system by accelerating Python, launching the Anaconda Toolbox and AI Navigator, and addressing the challenges of integrating data, code, and large‑language models for enterprise AI workloads.

AI NavigatorAI Operating SystemAnaconda

0 likes · 6 min read

How Anaconda Is Building an AI Operating System with High‑Performance Python

DataFunSummit

Jul 28, 2024 · Artificial Intelligence

Leveraging Large Language Models for Graph Learning: Opportunities, Current Progress, and Future Directions

This article reviews why large language models can be applied to graph learning, outlines their capabilities and graph data characteristics, surveys current research across different graph types and LLM roles, and proposes future research directions for unified cross‑domain graph learning.

AILarge Language ModelsMultimodal

0 likes · 19 min read

Leveraging Large Language Models for Graph Learning: Opportunities, Current Progress, and Future Directions

DataFunSummit

Jul 25, 2024 · Artificial Intelligence

LOGIN: Large‑Model‑Assisted Graph Neural Networks for User Behavior Risk Control

This article presents the latest advances from the Chinese Academy of Sciences in graph machine learning for user behavior risk control, introducing the LOGIN framework that leverages large language models as consultants to iteratively enhance GNN training, and demonstrates its effectiveness through extensive experiments on homogeneous and heterogeneous graph benchmarks.

Large Language ModelsUser Behaviorgraph neural networks

0 likes · 14 min read

LOGIN: Large‑Model‑Assisted Graph Neural Networks for User Behavior Risk Control