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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/abzb1/f-clip

공식 코드

Vision-Encoders (Already) Know What They See: Mitigating Object Hallucination via Simple Fine-Grained CLIPScore

https://github.com/achandlr/DEEP

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Detecting Errors through Ensembling Prompts (DEEP): An End-to-End LLM Framework for Detecting Factual Errors

https://github.com/adamian98/pulse

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

https://github.com/addleseehq/mpgt-eval

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Multi-party Goal Tracking with LLMs: Comparing Pre-training, Fine-tuning, and Prompt Engineering

https://github.com/adelaidehsu/rec

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Rate, Explain and Cite (REC): Enhanced Explanation and Attribution in Automatic Evaluation by Large Language Models

https://github.com/adelwang/migres

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LLMs Know What They Need: Leveraging a Missing Information Guided Framework to Empower Retrieval-Augmented Generation

https://github.com/agi-edgerunners/iil

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All in an Aggregated Image for In-Image Learning

https://github.com/ahnjaewoo/timechara

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TimeChara: Evaluating Point-in-Time Character Hallucination of Role-Playing Large Language Models

https://github.com/ai-med/halos

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HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery

https://github.com/ai4ce/occ4cast

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LiDAR-based 4D Occupancy Completion and Forecasting

https://github.com/aibee00/socraticquestioning

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Socratic Questioning: Learn to Self-guide Multimodal Reasoning in the Wild

https://github.com/aidaslab/second

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SECOND: Mitigating Perceptual Hallucination in Vision-Language Models via Selective and Contrastive Decoding

https://github.com/aim-uofa/omni-r1

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Omni-R1: Reinforcement Learning for Omnimodal Reasoning via Two-System Collaboration

https://github.com/ajjimeno/fast-evolutionary-evaluation

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Evolutionary thoughts: integration of large language models and evolutionary algorithms

https://github.com/ajjimeno/list-data

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Evolutionary thoughts: integration of large language models and evolutionary algorithms

https://github.com/ajjimeno/llm-gp

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Evolutionary thoughts: integration of large language models and evolutionary algorithms

https://github.com/aken12/LLM-based-QE-fails

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LLM-based Query Expansion Fails for Unfamiliar and Ambiguous Queries

https://github.com/akhadangi/noisefit

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Noise Augmented Fine Tuning for Mitigating Hallucinations in Large Language Models

https://github.com/alenai97/peft-mllm

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An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models

https://github.com/alibaba-nlp/omnisearch

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Benchmarking Multimodal Retrieval Augmented Generation with Dynamic VQA Dataset and Self-adaptive Planning Agent

https://github.com/alibaba/eigenscore

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INSIDE: LLMs' Internal States Retain the Power of Hallucination Detection

https://github.com/alibaba/struxgpt

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Enhancing LLM's Cognition via Structurization

https://github.com/alibabaresearch/damo-convai

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Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning

https://github.com/alsace08/sumcot

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Element-aware Summarization with Large Language Models: Expert-aligned Evaluation and Chain-of-Thought Method

https://github.com/amanuelf/biomed_abstractive_summarization

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Entity-driven Fact-aware Abstractive Summarization of Biomedical Literature

https://github.com/amazon-research/fact-check-summarization

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Entity-level Factual Consistency of Abstractive Text Summarization

https://github.com/amazon-science/SigExt

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Salient Information Prompting to Steer Content in Prompt-based Abstractive Summarization

https://github.com/amazon-science/crispo

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CriSPO: Multi-Aspect Critique-Suggestion-guided Automatic Prompt Optimization for Text Generation

https://github.com/amazon-science/handling-ontology-gaps-in-semantic-parsing

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Handling Ontology Gaps in Semantic Parsing

https://github.com/amazon-science/llm-hallucinations-factual-qa

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On Early Detection of Hallucinations in Factual Question Answering

https://github.com/amazon-science/mm-cot

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Multimodal Chain-of-Thought Reasoning in Language Models

https://github.com/amazon-science/refchecker

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RefChecker: Reference-based Fine-grained Hallucination Checker and Benchmark for Large Language Models

https://github.com/amazon-science/tofueval

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TofuEval: Evaluating Hallucinations of LLMs on Topic-Focused Dialogue Summarization

https://github.com/amazon-science/vigor

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ViGoR: Improving Visual Grounding of Large Vision Language Models with Fine-Grained Reward Modeling

https://github.com/amodaresi/memllm

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MemLLM: Finetuning LLMs to Use An Explicit Read-Write Memory

https://github.com/andrewzhe/lawyer-llama

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Lawyer LLaMA Technical Report

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