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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/gasolsun36/tog
공식 코드Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graph
https://github.com/gatenlp/topicllm_granularity_hallucination
공식 코드Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling
https://github.com/george0828zhang/sinkhorn-simultrans
공식 코드Anticipation-Free Training for Simultaneous Machine Translation
https://github.com/gersteinlab/struc-bench
공식 코드Struc-Bench: Are Large Language Models Really Good at Generating Complex Structured Data?
https://github.com/giskard-ai/phare
공식 코드https://github.com/git-disl/pokellmon
공식 코드PokeLLMon: A Human-Parity Agent for Pokemon Battles with Large Language Models
https://github.com/gmum/3d-point-clouds-autocomplete
공식 코드https://github.com/gmum/flysearch
공식 코드https://github.com/google/haloquest
공식 코드HaloQuest: A Visual Hallucination Dataset for Advancing Multimodal Reasoning
https://github.com/govtech-responsibleai/knowornot
공식 코드Know Or Not: a library for evaluating out-of-knowledge base robustness
https://github.com/graphml-lab-pwr/lapeig
공식 코드Hallucination Detection in LLMs Using Spectral Features of Attention Maps
https://github.com/gzcch/bingo
공식 코드Holistic Analysis of Hallucination in GPT-4V(ision): Bias and Interference Challenges
https://github.com/haesoochung/lan-hdr
공식 코드LAN-HDR: Luminance-based Alignment Network for High Dynamic Range Video Reconstruction
https://github.com/haiv-lab/llm-tmbr
공식 코드https://github.com/haizelabs/verdict
공식 코드https://github.com/halueval-wild/halueval-wild
공식 코드HaluEval-Wild: Evaluating Hallucinations of Language Models in the Wild
https://github.com/hanmenghan/mihio
공식 코드Skip \n: A Simple Method to Reduce Hallucination in Large Vision-Language Models
https://github.com/hanmenghan/skip-n
공식 코드Skip \n: A Simple Method to Reduce Hallucination in Large Vision-Language Models
https://github.com/hanqiu-hq/longhalqa
공식 코드LongHalQA: Long-Context Hallucination Evaluation for MultiModal Large Language Models
https://github.com/haochenliu2000/kelp
공식 코드Knowledge Graph-Enhanced Large Language Models via Path Selection
https://github.com/haodongli2024/rspope
공식 코드DDFAV: Remote Sensing Large Vision Language Models Dataset and Evaluation Benchmark
https://github.com/haotiansun14/adaplanner
공식 코드AdaPlanner: Adaptive Planning from Feedback with Language Models
https://github.com/haoyiq114/valor
공식 코드VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models
https://github.com/haoyu-bu/CAFe
공식 코드CAFe: Unifying Representation and Generation with Contrastive-Autoregressive Finetuning
https://github.com/haozhezhao/mic
공식 코드MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning
https://github.com/happen2me/cross-gnn
공식 코드GraphextQA: A Benchmark for Evaluating Graph-Enhanced Large Language Models
https://github.com/harderthenharder/transformers_tasks
공식 코드Do LLMs Possess a Personality? Making the MBTI Test an Amazing Evaluation for Large Language Models
https://github.com/hardik-uppal/teacher-student-gan
공식 코드Teacher-Student Adversarial Depth Hallucination to Improve Face Recognition
https://github.com/haroldchen19/vistadpo
공식 코드VistaDPO: Video Hierarchical Spatial-Temporal Direct Preference Optimization for Large Video Models
https://github.com/haronw/embodiedagent
공식 코드https://github.com/harrylaucngd/prompt-eng-master
공식 코드Integrating Chemistry Knowledge in Large Language Models via Prompt Engineering
https://github.com/hasanar1f/llava-hallunication-fix
공식 코드PAINT: Paying Attention to INformed Tokens to Mitigate Hallucination in Large Vision-Language Model
https://github.com/hasanar1f/paint
공식 코드PAINT: Paying Attention to INformed Tokens to Mitigate Hallucination in Large Vision-Language Model
https://github.com/hchoi256/i-halla-v1.0
공식 코드Evaluating Image Hallucination in Text-to-Image Generation with Question-Answering
https://github.com/hcy123902/atg-w-fg-rw
공식 코드Training Language Models to Generate Text with Citations via Fine-grained Rewards
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