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Hallucination
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/yellow-binary-tree/pfram
공식 코드Understanding Multimodal Hallucination with Parameter-Free Representation Alignment
https://github.com/yikai-wang/asuka-misato
공식 코드https://github.com/yilunlee/vcd_analysis
공식 코드Delve into Visual Contrastive Decoding for Hallucination Mitigation of Large Vision-Language Models
https://github.com/yinghaohu/legalhalbench
공식 코드Fine-tuning Large Language Models for Improving Factuality in Legal Question Answering
https://github.com/yinhanhe123/new_LLM4GNNExplanation
공식 코드https://github.com/yiren-jian/embedhalluc
공식 코드https://github.com/yiren-jian/labelhalluc
공식 코드https://github.com/yixinghuang/chatgpt-benchmark-on-radiation-oncology
공식 코드https://github.com/yiyangzhou/csr
공식 코드https://github.com/yiyangzhou/lure
공식 코드Analyzing and Mitigating Object Hallucination in Large Vision-Language Models
https://github.com/yiyangzhou/povid
공식 코드Aligning Modalities in Vision Large Language Models via Preference Fine-tuning
https://github.com/yoonnajang/rem
공식 코드Post-hoc Utterance Refining Method by Entity Mining for Faithful Knowledge Grounded Conversations
https://github.com/yrf1/normsage
공식 코드NormSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly
https://github.com/ysymyth/ReAct
공식 코드https://github.com/yuan-li-fnlp/r3-rag
공식 코드R3-RAG: Learning Step-by-Step Reasoning and Retrieval for LLMs via Reinforcement Learning
https://github.com/yuangan/VidFace
공식 코드VidFace: A Full-Transformer Solver for Video FaceHallucination with Unaligned Tiny Snapshots
https://github.com/yuchen814/codehalu
공식 코드CodeHalu: Investigating Code Hallucinations in LLMs via Execution-based Verification
https://github.com/yuchenliu98/comm
공식 코드From CLIP to DINO: Visual Encoders Shout in Multi-modal Large Language Models
https://github.com/yueche77/spin
공식 코드Mitigating Hallucinations in Vision-Language Models through Image-Guided Head Suppression
https://github.com/yueshengbin/SMART
공식 코드Synergistic Multi-Agent Framework with Trajectory Learning for Knowledge-Intensive Tasks
https://github.com/yuezih/less-is-more
공식 코드Less is More: Mitigating Multimodal Hallucination from an EOS Decision Perspective
https://github.com/yufang-liu/clip_hallucination
공식 코드Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models
https://github.com/yuki-asuuna/umwp
공식 코드Benchmarking Hallucination in Large Language Models based on Unanswerable Math Word Problem
https://github.com/yuqifan1117/hallucidoctor
공식 코드HalluciDoctor: Mitigating Hallucinatory Toxicity in Visual Instruction Data
https://github.com/yuxixie/v-dpo
공식 코드https://github.com/zcrwind/prefer
공식 코드PREFER: Prompt Ensemble Learning via Feedback-Reflect-Refine
https://github.com/zeju1997/data_halucination_teaching
공식 코드https://github.com/zetaalphavector/ragelo
공식 코드Evaluating RAG-Fusion with RAGElo: an Automated Elo-based Framework
https://github.com/zgmin/snse-cot
공식 코드Enhancing Semantics in Multimodal Chain of Thought via Soft Negative Sampling
https://github.com/zhangqijiang07/middle_layers_indicating_hallucinations
공식 코드https://github.com/zhenzhel/coba
공식 코드Correction with Backtracking Reduces Hallucination in Summarization
https://github.com/zhichaoxu-shufe/context-aware-decoding-qfs
공식 코드Context-aware Decoding Reduces Hallucination in Query-focused Summarization
https://github.com/zhitao-he/mmboundary
공식 코드https://github.com/zhiyuanpeng/eloq
공식 코드ELOQ: Resources for Enhancing LLM Detection of Out-of-Scope Questions
https://github.com/zhongmz/SiMT-Hallucination
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.