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Large Language Model

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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/levon003/wiki-ores-feedback

Retrieval-augmented Generation to Improve Math Question-Answering: Trade-offs Between Groundedness and Human Preference

https://github.com/lightaime/camel

CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society

https://github.com/linhaowei1/fine-tuning-scaling-law

Selecting Large Language Model to Fine-tune via Rectified Scaling Law

https://github.com/linzwcs/aft

Mixture-of-Agents Enhances Large Language Model Capabilities

https://github.com/litanlitudan/skyagi

Generative Agents: Interactive Simulacra of Human Behavior

https://github.com/liunian-harold-li/desco

DesCo: Learning Object Recognition with Rich Language Descriptions

https://github.com/liyan06/minicheck

RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models

https://github.com/llavar/llavar-2

TRINS: Towards Multimodal Language Models that Can Read

https://github.com/lllyasviel/omost

LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models

https://github.com/llm-lab-org/CLASP

Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection

https://github.com/llm4eval/LLMJudge

Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval

https://github.com/lluckyyh/xtrust

Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models

https://github.com/lm-sys/routellm

Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena

https://github.com/lmcache/lmcache

CacheGen: KV Cache Compression and Streaming for Fast Large Language Model Serving

https://github.com/lntzm/cvpr24track-longvideo

TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding

https://github.com/lucas-ventura/chapter-llama

Chapter-Llama: Efficient Chaptering in Hour-Long Videos with LLMs

https://github.com/lucidrains/muse-pytorch

Muse: Text-To-Image Generation via Masked Generative Transformers

https://github.com/lyqcom/mindspore_nlp_tutorial

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

https://github.com/m42-health/med42

Clinical Camel: An Open Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding

https://github.com/magicyang1573/llm-hardware-test-generation

VerilogReader: LLM-Aided Hardware Test Generation

https://github.com/marker-inc-korea/autorag

AutoRAG: Automated Framework for optimization of Retrieval Augmented Generation Pipeline

https://github.com/maziao/M2RAG

Multi-modal Retrieval Augmented Multi-modal Generation: A Benchmark, Evaluate Metrics and Strong Baselines

https://github.com/mediabrain-sjtu/medicalgpt-zh

MING-MOE: Enhancing Medical Multi-Task Learning in Large Language Models with Sparse Mixture of Low-Rank Adapter Experts

https://github.com/mem0ai/mem0

Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

https://github.com/microsoft/LLMLingua

MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention

https://github.com/microsoft/data-formulator

Data Formulator: AI-powered Concept-driven Visualization Authoring

https://github.com/microsoft/graphrag

From Local to Global: A Graph RAG Approach to Query-Focused Summarization

https://github.com/microsoft/mttl

Towards Modular LLMs by Building and Reusing a Library of LoRAs

https://github.com/microsoft/rd-agent

Towards Data-Centric Automatic R&D

https://github.com/microsoft/verus-proof-synthesis

AutoVerus: Automated Proof Generation for Rust Code

https://github.com/miguelgondu/finding_game_levels_paper

Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error

https://github.com/mindspore-courses/d2l-mindspore/tree/master/chapter_15_natural_language_processing_applications

Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks

https://github.com/mingdali6717/ensemble-of-retrievers

Generate rather than Retrieve: Large Language Models are Strong Context Generators

https://github.com/mit-han-lab/deepcompressor

QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving

https://github.com/mit-han-lab/lmquant

QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving

https://github.com/modelscope/modelscope

PolyLM: An Open Source Polyglot Large Language Model

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.