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Evaluation and Analysis of Hallucination in Large Vision-Language Models

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arxiv 2308.15126 v3 pith:CCLNX4XV submitted 2023-08-29 cs.LG cs.AIcs.CLcs.CV

Junyang Wang , Yiyang Zhou , Guohai Xu , Pengcheng Shi , Chenlin Zhao , Haiyang Xu , Qinghao Ye , Ming Yan
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Ji Zhang Jihua Zhu Jitao Sang Haoyu Tang
This is my paper · ORCID
classification cs.LGcs.AIcs.CLcs.CV
keywords hallucinationlvlmsevaluationhaelmlargemodelsdataproblem
verification ladder T0 review T1 audit T2 compute T3 formal
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Large Vision-Language Models (LVLMs) have recently achieved remarkable success. However, LVLMs are still plagued by the hallucination problem, which limits the practicality in many scenarios. Hallucination refers to the information of LVLMs' responses that does not exist in the visual input, which poses potential risks of substantial consequences. There has been limited work studying hallucination evaluation in LVLMs. In this paper, we propose Hallucination Evaluation based on Large Language Models (HaELM), an LLM-based hallucination evaluation framework. HaELM achieves an approximate 95% performance comparable to ChatGPT and has additional advantages including low cost, reproducibility, privacy preservation and local deployment. Leveraging the HaELM, we evaluate the hallucination in current LVLMs. Furthermore, we analyze the factors contributing to hallucination in LVLMs and offer helpful suggestions to mitigate the hallucination problem. Our training data and human annotation hallucination data will be made public soon.

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Cited by 10 Pith papers

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    cs.CV 2026-05 unverdicted novelty 6.5 of 10

    Object hallucinations in MLLMs track multi-head spatial inconsistency and temporal visual-attention fade; AFIP corrects both via cross-head enrichment and gated historical reinjection, reducing CHAIR/POPE rates training-free.

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    TruthLens fine-tunes LVLMs so the log-probability of a special token at each object mention becomes a truthfulness score, detecting object hallucinations with state-of-the-art AUROC.

  4. HalluScope: Fine-grained Hallucination Diagnosis for Multimodal Large Language Models

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    HalluScope couples span-level hallucination detection, 12-way type classification, and explanation generation in one model, and shows the resulting feedback reduces hallucinations in two MLLMs.

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