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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/helioszhao/shade

공식 코드

Style-Hallucinated Dual Consistency Learning for Domain Generalized Semantic Segmentation

https://github.com/helioszhao/shade-visualdg

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Style-Hallucinated Dual Consistency Learning: A Unified Framework for Visual Domain Generalization

https://github.com/hendryx-scale/mhal-detect

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Detecting and Preventing Hallucinations in Large Vision Language Models

https://github.com/hexuandeng/mono4simt

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Improving Simultaneous Machine Translation with Monolingual Data

https://github.com/heyuan9/medscore

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MedScore: Factuality Evaluation of Free-Form Medical Answers

https://github.com/hhy-huang/graphjudge

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Can LLMs be Good Graph Judge for Knowledge Graph Construction?

https://github.com/hhyhrhy/owmm-agent

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OWMM-Agent: Open World Mobile Manipulation With Multi-modal Agentic Data Synthesis

https://github.com/hillzhang1999/icd

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Alleviating Hallucinations of Large Language Models through Induced Hallucinations

https://github.com/hillzhang1999/llm-hallucination-survey

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Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models

https://github.com/hitum-dev/pollmgraph

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PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics

https://github.com/hiyouga/llama-factory

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Alleviating Hallucinations of Large Language Models through Induced Hallucinations

https://github.com/hkust-knowcomp/knowledge-constrained-decoding

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KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection

https://github.com/hkust-nlp/activation_decoding

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In-Context Sharpness as Alerts: An Inner Representation Perspective for Hallucination Mitigation

https://github.com/hkust-nlp/universal_truthfulness_hyperplane

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On the Universal Truthfulness Hyperplane Inside LLMs

https://github.com/hlchen23/verified

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VERIFIED: A Video Corpus Moment Retrieval Benchmark for Fine-Grained Video Understanding

https://github.com/hltchkust/chatgpt-evaluation

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A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity

https://github.com/holistic-ai/THaMES

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THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models

https://github.com/hongbinye/cognitive-mirage-hallucinations-in-llms

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Cognitive Mirage: A Review of Hallucinations in Large Language Models

https://github.com/hongcheng-gao/haven

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Exploring Hallucination of Large Multimodal Models in Video Understanding: Benchmark, Analysis and Mitigation

https://github.com/hqhbench/hqhbench

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Evaluating the Quality of Hallucination Benchmarks for Large Vision-Language Models

https://github.com/huage001/datasetfactorization

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Dataset Distillation via Factorization

https://github.com/huenao/debate-augmented-rag

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Removal of Hallucination on Hallucination: Debate-Augmented RAG

https://github.com/huggingface/distil-whisper

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Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling

https://github.com/huiminzeng/gpt-fedrec

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Federated Recommendation via Hybrid Retrieval Augmented Generation

https://github.com/huofushuo/SID

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Self-Introspective Decoding: Alleviating Hallucinations for Large Vision-Language Models

https://github.com/hyesunyun/medsysreviewsfromllms

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Appraising the Potential Uses and Harms of LLMs for Medical Systematic Reviews

https://github.com/hyperwjf/logiccheckgpt

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Logical Closed Loop: Uncovering Object Hallucinations in Large Vision-Language Models

https://github.com/ianhuang0630/aladdin

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Aladdin: Zero-Shot Hallucination of Stylized 3D Assets from Abstract Scene Descriptions

https://github.com/ibm-granite/granite-guardian

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Granite Guardian

https://github.com/ibm/sax4bpm

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How well can a large language model explain business processes as perceived by users?

https://github.com/ict-bigdatalab/rc-rag

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Controlling Risk of Retrieval-augmented Generation: A Counterfactual Prompting Framework

https://github.com/ict-bigdatalab/utility_judgments

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Are Large Language Models Good at Utility Judgments?

https://github.com/ictnlp/truthx

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TruthX: Alleviating Hallucinations by Editing Large Language Models in Truthful Space

https://github.com/idea-finai/tog

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Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph

https://github.com/ignisavium/causal-llava

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Causal-LLaVA: Causal Disentanglement for Mitigating Hallucination in Multimodal Large Language Models

https://github.com/iinemo/lm-polygraph

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Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

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