https://github.com/biocypher/chatgse
공식 코드A Platform for the Biomedical Application of Large Language Models
Papers with Code 아카이브
Retrieval-augmented Generation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/bithlp/homebench
공식 코드https://github.com/bohanhou14/clerc
공식 코드CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation
https://github.com/booydar/babilong
공식 코드BABILong: Testing the Limits of LLMs with Long Context Reasoning-in-a-Haystack
https://github.com/buaa-clab/circuitmind
공식 코드Towards Optimal Circuit Generation: Multi-Agent Collaboration Meets Collective Intelligence
https://github.com/buaadreamer/easyrag
공식 코드EasyRAG: Efficient Retrieval-Augmented Generation Framework for Automated Network Operations
https://github.com/bupt-gamma/pathrag
공식 코드PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths
https://github.com/buptwululu/fg-rag
공식 코드FG-RAG: Enhancing Query-Focused Summarization with Context-Aware Fine-Grained Graph RAG
https://github.com/byronbbl/ck-plug
공식 코드Parameters vs. Context: Fine-Grained Control of Knowledge Reliance in Language Models
https://github.com/byronbbl/context-dpo
공식 코드Context-DPO: Aligning Language Models for Context-Faithfulness
https://github.com/bytedance/imageref-vl
공식 코드ImageRef-VL: Enabling Contextual Image Referencing in Vision-Language Models
https://github.com/calubkk/raat
공식 코드Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training
https://github.com/camel-ai/vlm-play-starcraft2
공식 코드https://github.com/casparil/embedding-model-similarity
공식 코드Beyond Benchmarks: Evaluating Embedding Model Similarity for Retrieval Augmented Generation Systems
https://github.com/castorini/ragnarok
공식 코드Ragnarök: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track
https://github.com/charles-ydd/trojanrag
공식 코드TrojanRAG: Retrieval-Augmented Generation Can Be Backdoor Driver in Large Language Models
https://github.com/chen700564/RGB
공식 코드Benchmarking Large Language Models in Retrieval-Augmented Generation
https://github.com/chenneking/citegeist
공식 코드Citegeist: Automated Generation of Related Work Analysis on the arXiv Corpus
https://github.com/chenyiqun/mmoa-rag
공식 코드Improving Retrieval-Augmented Generation through Multi-Agent Reinforcement Learning
https://github.com/chenzhongwu20/rulerag_icl_ft
공식 코드RuleRAG: Rule-guided retrieval-augmented generation with language models for question answering
https://github.com/chiang-yuan/llamp
공식 코드https://github.com/chilab1/unipagpt-23
공식 코드Unipa-GPT: Large Language Models for university-oriented QA in Italian
https://github.com/chiral-carbon/kg-for-science
공식 코드What's In Your Field? Mapping Scientific Research with Knowledge Graphs and Large Language Models
https://github.com/chrismun/vvllm
공식 코드LLM4VV: Developing LLM-Driven Testsuite for Compiler Validation
https://github.com/chuhac/atm-rag
공식 코드ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator
https://github.com/cknievel/aitee-dataset
공식 코드https://github.com/claclark/cottontail
공식 코드https://github.com/clear-nus/edc
공식 코드Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction
https://github.com/cmavro/gnn-rag
공식 코드GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning
https://github.com/cnzzx/vsa
공식 코드Vision Search Assistant: Empower Vision-Language Models as Multimodal Search Engines
https://github.com/code-rag-bench/code-rag-bench
공식 코드https://github.com/codelion/optillm
공식 코드Eliciting Fine-Tuned Transformer Capabilities via Inference-Time Techniques
https://github.com/codermillat/StudyAbroadGPT
공식 코드A LoRA-Based Approach to Fine-Tuning LLMs for Educational Guidance in Resource-Constrained Settings
https://github.com/cods-gcs/chatty-gen
공식 코드Dialogue Benchmark Generation from Knowledge Graphs with Cost-Effective Retrieval-Augmented LLMs
https://github.com/cognitiveailab/matter-of-fact
공식 코드https://github.com/comp-well-org/esi
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.