https://github.com/hhhuang/cag
공식 코드Don't Do RAG: When Cache-Augmented Generation is All You Need for Knowledge Tasks
Papers with Code 아카이브
Large Language Model
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hhw-zhou/tsmmg
공식 코드Instruction Multi-Constraint Molecular Generation Using a Teacher-Student Large Language Model
https://github.com/hhy-huang/HiRAG
공식 코드https://github.com/hhy-huang/graphjudge
공식 코드Can LLMs be Good Graph Judge for Knowledge Graph Construction?
https://github.com/hi-weiyuan/PTF-FSR
공식 코드PTF-FSR: A Parameter Transmission-Free Federated Sequential Recommender System
https://github.com/hiagent2024/hiagent
공식 코드https://github.com/hieuphan33/mavl
공식 코드https://github.com/hieuthi/llamapartialspoof
공식 코드LlamaPartialSpoof: An LLM-Driven Fake Speech Dataset Simulating Disinformation Generation
https://github.com/hijkzzz/pymarl2
공식 코드Choices are More Important than Efforts: LLM Enables Efficient Multi-Agent Exploration
https://github.com/histmeisah/large-language-models-play-starcraftii
공식 코드Large Language Models Play StarCraft II: Benchmarks and A Chain of Summarization Approach
https://github.com/historicalink/LatamXIX
공식 코드Historical Ink: 19th Century Latin American Spanish Newspaper Corpus with LLM OCR Correction
https://github.com/hit-ices/tnnls-mtgn
공식 코드Who Should I Engage with At What Time? A Missing Event Aware Temporal Graph Neural Network
https://github.com/hitachi-nlp/larch
공식 코드LARCH: Large Language Model-based Automatic Readme Creation with Heuristics
https://github.com/hitz-zentroa/cn-eval
공식 코드A LLM-Based Ranking Method for the Evaluation of Automatic Counter-Narrative Generation
https://github.com/hitz-zentroa/gollie
공식 코드GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction
https://github.com/hitz-zentroa/whisper-lm
공식 코드Whisper-LM: Improving ASR Models with Language Models for Low-Resource Languages
https://github.com/hiyouga/llama-factory
공식 코드https://github.com/hkuds/autoagent
공식 코드AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents
https://github.com/hkuds/diffkg
공식 코드https://github.com/hkuds/lightrag
공식 코드https://github.com/hkuds/minirag
공식 코드MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation
https://github.com/hkuds/opengraph
공식 코드https://github.com/hkuds/videorag
공식 코드VideoRAG: Retrieval-Augmented Generation with Extreme Long-Context Videos
https://github.com/hkunlp/batch-prompting
공식 코드Batch Prompting: Efficient Inference with Large Language Model APIs
https://github.com/hkust-knowcomp/abductivekgr
공식 코드Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation
https://github.com/hkust-knowcomp/atomic-conceptualization
공식 코드Acquiring and Modelling Abstract Commonsense Knowledge via Conceptualization
https://github.com/hkust-knowcomp/autoschemakg
공식 코드https://github.com/hkust-knowcomp/ceqa
공식 코드Complex Query Answering on Eventuality Knowledge Graph with Implicit Logical Constraints
https://github.com/hkust-knowcomp/co2lm
공식 코드CoCoLM: COmplex COmmonsense Enhanced Language Model with Discourse Relations
https://github.com/hkust-knowcomp/efo-1-qa-benchmark
공식 코드Benchmarking the Combinatorial Generalizability of Complex Query Answering on Knowledge Graphs
https://github.com/hkust-knowcomp/efok-cqa
공식 코드$\text{EFO}_{k}$-CQA: Towards Knowledge Graph Complex Query Answering beyond Set Operation
https://github.com/hkust-knowcomp/eventground
공식 코드EventGround: Narrative Reasoning by Grounding to Eventuality-centric Knowledge Graphs
https://github.com/hkust-knowcomp/fit
공식 코드Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors
https://github.com/hkust-knowcomp/gold
공식 코드Gold: A Global and Local-aware Denoising Framework for Commonsense Knowledge Graph Noise Detection
https://github.com/hkust-knowcomp/lmpnn
공식 코드Logical Message Passing Networks with One-hop Inference on Atomic Formulas
https://github.com/hkust-knowcomp/mico
공식 코드MICO: A Multi-alternative Contrastive Learning Framework for Commonsense Knowledge Representation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.