Papers with Code 아카이브
Knowledge Graphs
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A knowledge graph is a structured representation of information that organizes data into nodes (entities) and edges (relationships) to show how different pieces of knowledge are interconnected. It enables enhanced data integration, search, and inference by modeling the relationships between concepts and entities in a graph format.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/holarissun/rewardmodelingbeyondbradleyterry
공식 코드https://github.com/holistic-ai/THaMES
공식 코드THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models
https://github.com/holmeswww/spring
공식 코드https://github.com/homan-lab/noise-audit-dataset
공식 코드https://github.com/homan-lab/voiced
공식 코드https://github.com/honee-w/u-sam
공식 코드U-SAM: An audio language Model for Unified Speech, Audio, and Music Understanding
https://github.com/honggen-zhang/hasa-ckge
공식 코드HaSa: Hardness and Structure-Aware Contrastive Knowledge Graph Embedding
https://github.com/hongwang600/flair
공식 코드Bot or Human? Detecting ChatGPT Imposters with A Single Question
https://github.com/hongzhiq/finegrainedsuicidedetection
공식 코드SOS-1K: A Fine-grained Suicide Risk Classification Dataset for Chinese Social Media Analysis
https://github.com/hoon-ock/catalystaigent
공식 코드https://github.com/hoorangyee/lrage
공식 코드https://github.com/hopkin-ghp/QuatDE
공식 코드QuatDE: Dynamic Quaternion Embedding for Knowledge Graph Completion
https://github.com/hornhehhf/rr
공식 코드Rethinking with Retrieval: Faithful Large Language Model Inference
https://github.com/hotbento/kg4receval
공식 코드KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?
https://github.com/hou27/recdap-fkgc
공식 코드https://github.com/houcharlie/pre-text
공식 코드PrE-Text: Training Language Models on Private Federated Data in the Age of LLMs
https://github.com/howardli0816/dual-fusion-diffusion
공식 코드https://github.com/hpcaitech/colossalai
공식 코드https://github.com/hpcaitech/elixir
공식 코드https://github.com/hplt-project/monolingual-multilingual-instruction-tuning
공식 코드Monolingual or Multilingual Instruction Tuning: Which Makes a Better Alpaca
https://github.com/hrishikeshrt/sangrahaka
공식 코드Sangrahaka: A Tool for Annotating and Querying Knowledge Graphs
https://github.com/hritikbansal/videocon
공식 코드VideoCon: Robust Video-Language Alignment via Contrast Captions
https://github.com/hrlics/lite
공식 코드LITE: Modeling Environmental Ecosystems with Multimodal Large Language Models
https://github.com/hse-cs/migtf
공식 코드Knowledge Graph Completion with Mixed Geometry Tensor Factorization
https://github.com/hsliu-initial/ctrla
공식 코드CtrlA: Adaptive Retrieval-Augmented Generation via Inherent Control
https://github.com/hsm316/llm_wikipedia
공식 코드https://github.com/hsu-aut/aml-shacl
공식 코드Automated Validation of Textual Constraints Against AutomationML via LLMs and SHACL
https://github.com/huangd1999/EmpiricalStudyofTestGeneration
공식 코드Measuring the Influence of Incorrect Code on Test Generation
https://github.com/huangl22/chat3gpp
공식 코드Chat3GPP: An Open-Source Retrieval-Augmented Generation Framework for 3GPP Documents
https://github.com/huangyuxiang03/Locret
공식 코드https://github.com/huawei-lin/rapidin
공식 코드Token-wise Influential Training Data Retrieval for Large Language Models
https://github.com/huawei-noah/hebo
공식 코드ROS-LLM: A ROS framework for embodied AI with task feedback and structured reasoning
https://github.com/huda-hakami/learning-to-borrow-for-kgs
공식 코드https://github.com/huenao/debate-augmented-rag
공식 코드Removal of Hallucination on Hallucination: Debate-Augmented RAG
https://github.com/huggingface/autotrain-advanced
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.