large-language-models — 8 Technical Articles

Mar 17, 2026 · Interview Experience

Expert Links Microservices to Financial AI: Architecture and Data Governance

In this interview, senior technology specialist Chen Ke shares how he adapts internet‑scale microservice and PaaS practices to the highly regulated financial sector, discusses building enterprise knowledge‑base platforms with large language models, outlines data‑governance and compliance strategies, and predicts the evolving skill set engineers will need.

AIMicroservicesdata governance

0 likes · 15 min read

Expert Links Microservices to Financial AI: Architecture and Data Governance

Meituan Technology Team

Oct 9, 2025 · Artificial Intelligence

How VSRM Cuts Redundant Reasoning Steps in Large Language Models

The paper introduces VSRM, a verifiable step‑reward mechanism that penalizes ineffective reasoning steps and rewards useful ones in large language model inference, dramatically shortening output length while preserving or even improving performance across multiple benchmarks and reinforcement‑learning algorithms.

AIEfficient Inferencelarge-language-models

0 likes · 10 min read

How VSRM Cuts Redundant Reasoning Steps in Large Language Models

Architecture and Beyond

Feb 22, 2025 · Artificial Intelligence

Understanding Retrieval‑Augmented Generation (RAG) and Its Role in Enhancing Large Language Models

The article explains how the inherent knowledge‑staleness, hallucination, lack of private data, non‑traceable output, limited long‑text handling, and data‑security concerns of large language models can be mitigated by Retrieval‑Augmented Generation, which combines external retrieval, augmentation, and generation to provide up‑to‑date, reliable, and secure AI responses.

AIKnowledge augmentationLLM

0 likes · 15 min read

Understanding Retrieval‑Augmented Generation (RAG) and Its Role in Enhancing Large Language Models

Architecture Digest

Feb 7, 2025 · Artificial Intelligence

Open-Source Replication of OpenAI’s o1 Model Achieves Superior Performance with Minimal Cost

A recent study by Fei‑Fei Li’s team shows that using supervised fine‑tuning on the open‑source Qwen2.5‑32B‑Instruct model can replicate and even surpass the reasoning abilities of OpenAI’s o1‑preview at a fraction of the computational cost, demonstrating a cheap yet powerful approach to large‑language‑model development.

Supervised Fine‑Tuningbudget-forcingcost-effective-ai

0 likes · 6 min read

Open-Source Replication of OpenAI’s o1 Model Achieves Superior Performance with Minimal Cost

Alibaba Cloud Big Data AI Platform

Nov 6, 2024 · Artificial Intelligence

Unlocking Long-Text Video Understanding and LLM Distillation with Alibaba PAI

Alibaba Cloud’s AI platform PAI recently saw two papers accepted at EMNLP2024—VideoCLIP‑XL, which enhances video‑text representation for long descriptions using a large video‑long‑description dataset and novel pre‑training tasks, and TAPIR, a curriculum‑planning framework that distills instruction‑following abilities of large language models—while also releasing associated models, datasets, and integration tools for users.

DistillationEMNLP2024large-language-models

0 likes · 8 min read

Unlocking Long-Text Video Understanding and LLM Distillation with Alibaba PAI

DataFunTalk

Sep 16, 2024 · Artificial Intelligence

Integrating Large Language Models into Health E‑commerce Recommendation Systems: Development, Challenges, and Practical Deployments

This article reviews the evolution of large‑model recommendation techniques, analyzes the specific demands and obstacles of health‑focused e‑commerce, and details JD Health's practical implementations—including LLM‑enhanced recall, deep item‑to‑item models, and scaling‑law‑driven CTR improvements—while discussing open research questions and future directions.

CTRHealthcareLLM-enhancement

0 likes · 17 min read

Integrating Large Language Models into Health E‑commerce Recommendation Systems: Development, Challenges, and Practical Deployments

Kuaishou Tech

May 20, 2024 · Artificial Intelligence

Kuaishou’s AI XiaoKuai: Technical Innovations Behind the Consumer‑Entertainment Assistant

The article reviews the evolution of large‑model technology, details Kuaishou’s self‑developed “Kuaishou Yi” model architecture, data pipeline, evaluation benchmarks, and explains how the AI XiaoKuai assistant achieves multimodal, personable interactions while also announcing related recruitment opportunities.

AI AssistantKuaishoudialogue systems

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Kuaishou’s AI XiaoKuai: Technical Innovations Behind the Consumer‑Entertainment Assistant

DataFunSummit

Nov 13, 2023 · Artificial Intelligence

SWIFT: A Scalable Light‑Weight Training and Inference Framework for Efficient Model Fine‑Tuning

SWIFT is an open‑source, PyTorch‑based framework that integrates multiple efficient fine‑tuning methods such as LoRA, QLoRA, Adapter, and the proprietary ResTuning, enabling developers to fine‑tune large language and multimodal models on consumer‑grade GPUs with significantly reduced memory and compute requirements.

Fine-tuningLoRAModelScope

0 likes · 13 min read

SWIFT: A Scalable Light‑Weight Training and Inference Framework for Efficient Model Fine‑Tuning