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Hallucination

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/thudm/agenttuning

ReAct: Synergizing Reasoning and Acting in Language Models

https://github.com/tidedra/vl-rlhf

RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback

https://github.com/toneli/rt-retrieving-and-thinking

GPT-NER: Named Entity Recognition via Large Language Models

https://github.com/tongjingqi/code2logic

SWIFT:A Scalable lightWeight Infrastructure for Fine-Tuning

https://github.com/tyliupku/HaDeS

A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation

https://github.com/uiuc-comp-imaging-sci/hallucinations-tomo-recon

On hallucinations in tomographic image reconstruction

https://github.com/vanderpoelliam/cpmi

Detecting Hallucinated Content in Conditional Neural Sequence Generation

https://github.com/vortezwohl/Autono

ReAct: Synergizing Reasoning and Acting in Language Models

https://github.com/wangrongsheng/caregpt

Evaluating Hallucinations in Chinese Large Language Models

https://github.com/xinding-sys/StreamMind

Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding

https://github.com/xqx12/daily-info

Multimodal Chain-of-Thought Reasoning in Language Models

https://github.com/yenchehsiao/autonomousllmagentwithadaptingplanning

ReAct: Synergizing Reasoning and Acting in Language Models

https://github.com/ytsheng/sketch2cat-pulse

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

https://github.com/zeroxleo/ml-agent

MLAgentBench: Evaluating Language Agents on Machine Learning Experimentation

https://github.com/zhuang-li/factual

DiscoSG: Towards Discourse-Level Text Scene Graph Parsing through Iterative Graph Refinement

https://github.com/ziwei-zheng/nullu

Evaluating Object Hallucination in Large Vision-Language Models

https://github.com/zli12321/qa_metrics

HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models

https://github.com/znlp/language-imbalance-driven-rewarding

DEEM: Diffusion Models Serve as the Eyes of Large Language Models for Image Perception

https://huggingface.co/spaces/NCSOFT/harim_plus

HaRiM$^+$: Evaluating Summary Quality with Hallucination Risk

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.