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Ximalaya Technology Team
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
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
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 ModelsParallelism
0 likes · 28 min read
Boost LLM Training on Massive Clusters with DP/TP Overlap and Context Parallelism
HyperAI Super Neural
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.

Knowledge GraphsLarge Language ModelsOpen Datasets
0 likes · 14 min read
From Computer Vision to Medical AI: Prof. Xie's Work Hits Nature, NeurIPS, CVPR
DataFunSummit
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
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
NewBeeNLP
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.

FairnessKDD2024Large Language Models
0 likes · 15 min read
What’s Trending in Recommendation Systems at KDD 2024? A Comprehensive Paper Overview
Tencent Docs Tech Team
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
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-ThoughtFew‑Shot Learning
0 likes · 26 min read
Prompt Engineering: Concepts, Evolution, Techniques, and a Logistics Application Case
DataFunSummit
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.

AIGraphRAGLarge Language Models
0 likes · 16 min read
GraphRAG: Using Graph Structures to Enhance Retrieval‑Augmented Generation – Challenges, Methods, and Product Deployments
Baobao Algorithm Notes
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
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
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 IntelligenceContent Generation
0 likes · 9 min read
Applying AIGC to Transform Insurance Marketing at Ant Group
Fighter's World
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
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
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 ModelsNLPToken
0 likes · 5 min read
What Is a Token in Large Language Models?
AntTech
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
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.

AICIOKnowledge Base
0 likes · 14 min read
How AI Is Redefining the Enterprise CIO Role – Insights from Alibaba Cloud’s CIO
Fighter's World
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 CloudCloud Services
0 likes · 8 min read
Key Considerations for Deploying Large Language Models in Cloud Services
AntTech
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
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.

Generative RetrievalHigh‑performance computingLarge Language Models
0 likes · 17 min read
Generative Retrieval Based on Yuan Large Model: Implementation and Practice in Tencent Advertising
360 Zhihui Cloud Developer
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
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.

BenchmarkingLarge Language ModelsLlama 3
0 likes · 32 min read
Inside Llama 3: Training, Architecture, and Performance Secrets
Baobao Algorithm Notes
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.

InferenceLarge Language ModelsMCTS
0 likes · 26 min read
How MCTS Powers Inference in OpenAI’s o1: A Deep Dive with rStar
Architect
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.

Few‑Shot LearningLarge Language ModelsModel Parameters
0 likes · 28 min read
Master Prompt Engineering: A Universal Framework for Building Effective LLM Prompts
DataFunSummit
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
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.

InferenceLarge Language ModelsOpenAI
0 likes · 35 min read
How Does OpenAI’s o1 Model Leverage Self‑Play RL and New Scaling Laws?
Tencent Cloud Developer
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 OptimizationFew‑Shot LearningLarge Language Models
0 likes · 26 min read
A Comprehensive Prompt Engineering Framework: Universal Templates, RAG, Few‑Shot, Memory, and Automated Optimization
AntData
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.

AILarge Language ModelsRAG
0 likes · 6 min read
DB-GPT: Open-Source AI-Native Data Application Development Framework
DataFunTalk
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
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
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 EvaluationAI agentsEnterprise AI
0 likes · 15 min read
Building an LLM-Based Agent Platform for Enterprise Commercialization: Strategies, Architecture, and Practical Insights
Baidu Geek Talk
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
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.

Deep LearningGPULarge Language Models
0 likes · 29 min read
How Distributed Training Powers Massive Language Models: Concepts, Strategies, and Code
DataFunSummit
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
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
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 ArchitectureLarge Language Models
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
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
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.

Data PlatformLarge Language ModelsRetrieval Augmented Generation
0 likes · 37 min read
How a R&D Data Platform Leverages Large Language Models to Accelerate Issue Diagnosis
DataFunTalk
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
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
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 ModelsMultimodal AI
0 likes · 13 min read
Building Multi‑Scenario AI Assistants with Large Models at Huolala
Baobao Algorithm Notes
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
Efficient Ops
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
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
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 IntelligenceData Analytics
0 likes · 18 min read
AI-Driven BI: Achieving Zero-Barrier Data Access and Smart Insights
Baidu Geek Talk
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
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
DataFunTalk
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
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 ArchitectureChatGPTIndustrial AI
0 likes · 13 min read
Applying Large Language Models to Automotive Industrialization: Practices and Experiences
Alibaba Cloud Developer
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
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 ModelsMultimodal Retrieval
0 likes · 10 min read
How Large Language Models Automate Order Cancellation Responsibility at HuoLala
DaTaobao Tech
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
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 OpsLarge Language ModelsOperations
0 likes · 19 min read
How Large Language Models Can Transform Ops Fault Handling: A Practical Guide
Ops Development & AI Practice
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
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
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
AntTech
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.

ACL2024Information ExtractionLarge Language Models
0 likes · 20 min read
Ant Group Contributions to ACL 2024: Summaries of 14 Accepted Papers Across NLP and AI
DaTaobao Tech
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 ModelsRAG
0 likes · 22 min read
Challenges and Optimization Techniques for Retrieval‑Augmented Generation (RAG)
DataFunSummit
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
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
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.

AILarge Language Modelsevent recap
0 likes · 8 min read
Large Model Innovations Redefining Frontend Development – Key Takeaways
Software Development Quality
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
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.

Large Language ModelsRecommendation Systemsfinancial fraud detection
0 likes · 4 min read
Graph Technology Overview and Applications – From GraphGPT to Graph Databases
NewBeeNLP
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
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
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
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
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
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
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
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
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 Modelsgraph neural networksmachine learning
0 likes · 14 min read
LOGIN: Large‑Model‑Assisted Graph Neural Networks for User Behavior Risk Control
21CTO
21CTO
Jul 24, 2024 · Artificial Intelligence

Meta’s Llama 3.1 405B: How the Open‑Source Giant Stands Up to GPT‑4 and Claude 3.5

Meta’s newly released Llama 3.1 series, highlighted by the 405B model trained on 150 trillion tokens, claims state‑of‑the‑art performance in coding, mathematics, and multilingual summarization while offering an open‑source alternative to GPT‑4o and Claude 3.5 Sonnet.

AI competitionLarge Language ModelsLlama 3.1
0 likes · 6 min read
Meta’s Llama 3.1 405B: How the Open‑Source Giant Stands Up to GPT‑4 and Claude 3.5
Kuaishou Tech
Kuaishou Tech
Jul 23, 2024 · Artificial Intelligence

Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models

This paper introduces Parrot, a system that enhances large language models' (LLMs) multi-turn instruction following capabilities through context-aware preference optimization (CaPO) and synthetic data generation, achieving significant performance improvements with limited training data.

CaPOLarge Language ModelsNLP
0 likes · 9 min read
Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models
DataFunTalk
DataFunTalk
Jul 21, 2024 · Artificial Intelligence

Integrating DataOps with Large Language Models for Text2SQL: Practices, Challenges, and Future Directions

This article presents a comprehensive overview of how DataOps principles combined with large language models such as GPT‑4 enable more agile and intelligent data engineering workflows, focusing on Text2SQL applications, schema‑linking techniques, practical product implementations, and future research challenges.

AIDataOpsLarge Language Models
0 likes · 23 min read
Integrating DataOps with Large Language Models for Text2SQL: Practices, Challenges, and Future Directions
IT Services Circle
IT Services Circle
Jul 17, 2024 · Artificial Intelligence

Why Large Language Models Mistake 9.11 > 9.9: Prompting, Tokenizer Effects, and Recent Findings

The article examines why leading large language models such as GPT‑4o, Gemini Advanced, and Claude 3.5 incorrectly claim that 9.11 is larger than 9.9, analyzes tokenization and prompting strategies that cause the error, and discusses recent research and OpenAI model updates.

AI reasoningLarge Language ModelsNumerical Comparison
0 likes · 7 min read
Why Large Language Models Mistake 9.11 > 9.9: Prompting, Tokenizer Effects, and Recent Findings
Java Tech Enthusiast
Java Tech Enthusiast
Jul 12, 2024 · Artificial Intelligence

Why Alibaba’s Qwen‑2 Is Outperforming Global LLMs and What It Means for AI

After OpenAI halted API access in China, Alibaba’s Tongyi Qwen‑2 quickly rose to the top of global open‑source LLM leaderboards, surpassing Meta’s Llama‑3 and other contenders, with detailed benchmark scores, performance gains over previous versions, and implications for China’s AI ecosystem.

AI BenchmarkAlibabaChina AI
0 likes · 5 min read
Why Alibaba’s Qwen‑2 Is Outperforming Global LLMs and What It Means for AI
Kuaishou Large Model
Kuaishou Large Model
Jul 11, 2024 · Artificial Intelligence

Pipeline-Aware Offloading & Balanced Checkpointing Accelerate LLM Training

Researchers from Kwai’s large-model team present a novel training system that combines pipeline-parallel-aware activation offloading with a compute-memory balanced checkpointing strategy, enabling lossless acceleration of large language models, achieving up to 42.7% MFU on 256 NVIDIA H800 GPUs while reducing memory usage.

GPU trainingKwaiLarge Language Models
0 likes · 13 min read
Pipeline-Aware Offloading & Balanced Checkpointing Accelerate LLM Training
JD Tech
JD Tech
Jul 11, 2024 · Artificial Intelligence

Intelligent Parcel Identification in JD Express Logistics Using Large Language Models

This article examines the challenges of low parcel matching rates in JD Express logistics and proposes a large‑model‑based intelligent identification system, detailing its architecture, accuracy validation, cost‑saving cache strategy, and future prospects for improved efficiency and personalized services.

AI in e-commerceLarge Language ModelsLogistics
0 likes · 24 min read
Intelligent Parcel Identification in JD Express Logistics Using Large Language Models
NewBeeNLP
NewBeeNLP
Jul 10, 2024 · Artificial Intelligence

Can Large Language Models Master Co‑Temporal Reasoning? Introducing COTEMPQA

This article presents the COTEMPQA benchmark for evaluating large language models on co‑temporal reasoning, details its four scenario types, construction pipeline, experimental results across models, error analysis, and proposes the MR‑COT strategy that leverages mathematical reasoning to significantly improve performance.

LLM evaluationLarge Language ModelsMR-COT
0 likes · 11 min read
Can Large Language Models Master Co‑Temporal Reasoning? Introducing COTEMPQA
DataFunSummit
DataFunSummit
Jul 9, 2024 · Artificial Intelligence

Applying Large Language Models to Recommendation Systems at Ant Group

This article details Ant Group's research on integrating large language models into recommendation pipelines, covering background challenges, knowledge extraction, teacher‑student distillation, experimental results, and practical Q&A for improving bias, efficiency, and cold‑start performance.

AI researchAnt GroupLarge Language Models
0 likes · 14 min read
Applying Large Language Models to Recommendation Systems at Ant Group
DataFunSummit
DataFunSummit
Jul 6, 2024 · Artificial Intelligence

Synergy Between Large Language Models and Knowledge Graphs: Recent Advances, Evaluation, and Future Integration

This article reviews the rapid progress of large language models and their complementary relationship with knowledge graphs, covering comparative strengths, knowledge extraction and completion, evaluation benchmarks, deployment benefits, complex reasoning support, and prospects for interactive fusion toward more reliable and explainable AI systems.

AI EvaluationKnowledge GraphsLarge Language Models
0 likes · 12 min read
Synergy Between Large Language Models and Knowledge Graphs: Recent Advances, Evaluation, and Future Integration
21CTO
21CTO
Jul 5, 2024 · Artificial Intelligence

15 Real-World Ways Companies Leverage Large Language Models

This article explores fifteen detailed examples of how major companies across sectors—from streaming and e‑commerce to transportation and social platforms—are harnessing large language models to improve search, personalize communications, detect fraud, and enhance operational efficiency.

AI case studiesEnterprise AILLM applications
0 likes · 9 min read
15 Real-World Ways Companies Leverage Large Language Models
Bilibili Tech
Bilibili Tech
Jul 5, 2024 · Artificial Intelligence

Bilibili's AI Innovations at WAIC 2024: Empowering Creators and Transforming Content

At WAIC 2024, Bilibili unveiled a suite of AI tools—including a 1:1 digital‑avatar generator, dynamic comic technology, a customized voice library for virtual singer Luo Tianyi, the BiliStudio video‑audio model, and the Index‑1.9B large‑language models—empowering creators, cutting production costs, and serving its 80 million‑plus monthly users with advanced content‑creation and commercial‑marketing solutions.

AI content creationAI marketingBilibili
0 likes · 7 min read
Bilibili's AI Innovations at WAIC 2024: Empowering Creators and Transforming Content
JD Tech
JD Tech
Jul 5, 2024 · Artificial Intelligence

Generative Recommendation Systems for JD Alliance Advertising: Architecture, Implementation, and Experimental Evaluation

This article surveys how large language models reshape recommendation systems, presents a generative RS framework tailored for JD Alliance advertising, details material representation, model input, training and inference pipelines, and reports extensive offline and online experiments demonstrating its effectiveness on sparse user data.

Generative RecommendationLLMLarge Language Models
0 likes · 27 min read
Generative Recommendation Systems for JD Alliance Advertising: Architecture, Implementation, and Experimental Evaluation
Meituan Technology Team
Meituan Technology Team
Jul 4, 2024 · Artificial Intelligence

Meituan Search Advertising: Evolution of Recall Strategies and Generative Approaches

Meituan’s search advertising has progressed from rule‑based keyword mining to hierarchical recall that partitions traffic and supply, and now to generative recall using large language models, chain‑of‑thought generation, diffusion‑enhanced multimodal vectors, and knowledge distillation, expanding the decision space while tackling compute and ROI challenges.

Generative ModelsLarge Language ModelsMeituan
0 likes · 19 min read
Meituan Search Advertising: Evolution of Recall Strategies and Generative Approaches
360 Smart Cloud
360 Smart Cloud
Jul 4, 2024 · Artificial Intelligence

Optimizing Mixture-of-Experts (MoE) Training with the QLM Framework

This article introduces the background and challenges of large language model training, explains the Mixture-of-Experts (MoE) architecture, and details several optimization techniques implemented in the QLM framework—including fine-grained and shared experts, top‑k gating, token distribution, expert parallelism, and grouped GEMM – to improve training efficiency and performance.

AIDistributed TrainingLarge Language Models
0 likes · 10 min read
Optimizing Mixture-of-Experts (MoE) Training with the QLM Framework
NewBeeNLP
NewBeeNLP
Jul 3, 2024 · Industry Insights

What Dominated the AI Landscape in Q2 2024? From Llama 3 to GPT‑4o and Global Price Wars

The second quarter of 2024 saw a whirlwind of AI developments—including Meta’s open‑source Llama 3, Microsoft’s fleeting WizardLM‑2, a wave of model price cuts, major IPOs, legislative restrictions, and the debut of OpenAI’s multimodal GPT‑4o—painting a vivid picture of rapid innovation, fierce competition, and shifting market dynamics across the global AI ecosystem.

AI modelsAI policyLarge Language Models
0 likes · 24 min read
What Dominated the AI Landscape in Q2 2024? From Llama 3 to GPT‑4o and Global Price Wars
JD Cloud Developers
JD Cloud Developers
Jul 2, 2024 · Operations

How Large Language Models Are Transforming Modern IT Operations

From manual server management to automated scripts, AIOps, and ChatOps, this article traces the evolution of IT operations and demonstrates how large language models boost efficiency, enable intelligent assistants, automated diagnostics, and smart log analysis, aiming for rapid fault detection, localization, and resolution.

ChatOpsLarge Language ModelsOperations
0 likes · 7 min read
How Large Language Models Are Transforming Modern IT Operations
Continuous Delivery 2.0
Continuous Delivery 2.0
Jul 2, 2024 · Artificial Intelligence

Dynamic Integrated Developer Activity (DIDACT): Large Sequence Models for Software Development

The article introduces DIDACT, a large‑scale multitask machine‑learning framework that trains on the full software‑development workflow—including edits, builds, reviews, and tool interactions—to create AI assistants that can predict and suggest developer actions throughout the coding process.

AI for CodeLarge Language Modelsdeveloper assistance
0 likes · 11 min read
Dynamic Integrated Developer Activity (DIDACT): Large Sequence Models for Software Development
DaTaobao Tech
DaTaobao Tech
Jul 1, 2024 · Artificial Intelligence

Recent Progress in Vision-Language Models (VLMs)

Over the past year, Vision‑Language Models have surged from early multimodal experiments to competitive open‑source systems rivaling GPT‑4, driven by higher‑resolution processing, richer vision encoders, better projection layers, and larger curated datasets, yet they still face evaluation difficulties, hallucinations, speed limits, and limited multimodal output.

Computer VisionDeep LearningLarge Language Models
0 likes · 24 min read
Recent Progress in Vision-Language Models (VLMs)
JD Tech
JD Tech
Jun 28, 2024 · Artificial Intelligence

An Overview of Large Language Models: History, Fundamentals, Prompt Engineering, Retrieval‑Augmented Generation, Agents, and Multimodal AI

This article provides a comprehensive introduction to large language models, covering their historical development, core architecture, training process, prompt engineering techniques, Retrieval‑Augmented Generation, agent frameworks, multimodal capabilities, safety challenges, and future research directions.

AI SafetyAI agentsDeep Learning
0 likes · 22 min read
An Overview of Large Language Models: History, Fundamentals, Prompt Engineering, Retrieval‑Augmented Generation, Agents, and Multimodal AI
DataFunSummit
DataFunSummit
Jun 26, 2024 · Artificial Intelligence

2026 Roadmap for Recommendation Systems: Challenges, Research Directions, and OneRec Integration

This article outlines the current bottlenecks of conventional recommendation pipelines and proposes a comprehensive 2026 research agenda covering retention improvement, user growth, content ecosystem, multi‑objective Pareto optimization, long‑term value modeling, whole‑site optimization, interactive recommendation, personalized modeling, decision‑theoretic formulation, and the OneRec multi‑source fusion framework.

Large Language ModelsUser Retentionmulti-objective optimization
0 likes · 18 min read
2026 Roadmap for Recommendation Systems: Challenges, Research Directions, and OneRec Integration