https://github.com/ozzzp/ternary_decompose
공식 코드Ternary Singular Value Decomposition as a Better Parameterized Form in Linear Mapping
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/pabloruizponce/in2IN
공식 코드in2IN: Leveraging individual Information to Generate Human INteractions
https://github.com/padas-lab-de/PersonaRAG
공식 코드PersonaRAG: Enhancing Retrieval-Augmented Generation Systems with User-Centric Agents
https://github.com/pair-code/pretraining-tda
공식 코드Scalable Influence and Fact Tracing for Large Language Model Pretraining
https://github.com/paitesanshi/llm-agent-survey
공식 코드https://github.com/paitesanshi/set
공식 코드Investigating and Extending Homans' Social Exchange Theory with Large Language Model based Agents
https://github.com/pan-zihao/sca
공식 코드SCA: Improve Semantic Consistent in Unrestricted Adversarial Attacks via DDPM Inversion
https://github.com/panruotong/cag
공식 코드Not All Contexts Are Equal: Teaching LLMs Credibility-aware Generation
https://github.com/paperswithcode/galai
공식 코드https://github.com/parallelcodefoundry/ParEval
공식 코드https://github.com/parameterlab/trap
공식 코드TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification
https://github.com/pardissz/translate-with-care
공식 코드https://github.com/particlemedia/ragtruth
공식 코드RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
https://github.com/pasquini-dario/LLMmap
공식 코드https://github.com/pasquini-dario/llm_neuralexec
공식 코드Neural Exec: Learning (and Learning from) Execution Triggers for Prompt Injection Attacks
https://github.com/passing2961/thanos
공식 코드Thanos: Enhancing Conversational Agents with Skill-of-Mind-Infused Large Language Model
https://github.com/pat-jj/KARE
공식 코드Reasoning-Enhanced Healthcare Predictions with Knowledge Graph Community Retrieval
https://github.com/pat-jj/Retrieval-And-Structure
공식 코드RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation
https://github.com/pat-jj/ras
공식 코드RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generation
https://github.com/pat-jj/s3
공식 코드s3: You Don't Need That Much Data to Train a Search Agent via RL
https://github.com/paul-rottger/safetyprompts-paper
공식 코드https://github.com/paul-sheridan/paper-heaps-law-llm
공식 코드https://github.com/pauldrm/targeted_intervention
공식 코드HSI: Head-Specific Intervention Can Induce Misaligned AI Coordination in Large Language Models
https://github.com/paulgavrikov/vlm_shapebias
공식 코드https://github.com/paxnea/genAI-rngt
공식 코드A Comparison of Large Language Model and Human Performance on Random Number Generation Tasks
https://github.com/pbihao/slm
공식 코드https://github.com/pdx97/sbi-rag_neurips2024
공식 코드https://github.com/pedrocurvo/thetraitors
공식 코드The Traitors: Deception and Trust in Multi-Agent Language Model Simulations
https://github.com/peijiez/intentrec4maps
공식 코드Intention Recognition in Real-Time Interactive Navigation Maps
https://github.com/pengboci/graphrag-survey
공식 코드https://github.com/pengshuai-rin/multimath
공식 코드MultiMath: Bridging Visual and Mathematical Reasoning for Large Language Models
https://github.com/pengxiao-song/lawgpt
공식 코드LawGPT: A Chinese Legal Knowledge-Enhanced Large Language Model
https://github.com/perceptioncomputinglab/medfilip
공식 코드https://github.com/personnoname/fgeodrl
공식 코드FGeo-DRL: Deductive Reasoning for Geometric Problems through Deep Reinforcement Learning
https://github.com/petergriffinjin/search-r1
공식 코드Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning
https://github.com/petrosrapto/pakton
공식 코드PAKTON: A Multi-Agent Framework for Question Answering in Long Legal Agreements
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.