| Challenge: | Existing studies find VH instances only in existing image datasets, which results in biased understanding of MLLMs’ performance under VH. |
| Approach: | They propose a tool called VHTest to generate a diverse set of VH instances from existing image datasets and a text-to-image generative model to generate VH images based on the text descriptions. |
| Outcome: | The proposed tool finds VH instances in existing image datasets and generates images based on the text descriptions. |
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| Challenge: | a comprehensive and fine-grained measurement of the hallucination is crucial for LLMs' wide applications. |
| Approach: | They propose a dataset that offers ANalytical Annotation of Hallucinations in Large Language Models. |
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Jinghan He, Kuan Zhu, Haiyun Guo, Junfeng Fang, Zhenglin Hua, Yuheng Jia, Ming Tang, Tat-Seng Chua, Jinqiao Wang
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FIHA: Automated Fine-grained Hallucinations Evaluations in Large Vision Language Models with Davidson Scene Graphs (2025.findings-acl)
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| Challenge: | Current approaches to large vision-language models rely on costly annotations and are not comprehensive in terms of evaluating all aspects. |
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| Challenge: | Despite the significant success of large vision-language models, some studies have revealed that LVLMs suffer from the hallucination problem when given long-term misleading textual history. |
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| Challenge: | Multi-modal large language models (MLLMs) generate plausible but incorrect content, resulting in hallucinations . recent advances in MLLM technology have demonstrated their outstanding performance in a variety of visual tasks, such as object detection. |
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Xiyang Wu, Tianrui Guan, Dianqi Li, Shuaiyi Huang, Xiaoyu Liu, Xijun Wang, Ruiqi Xian, Abhinav Shrivastava, Furong Huang, Jordan Boyd-Graber, Tianyi Zhou, Dinesh Manocha
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HALoGEN: Fantastic LLM Hallucinations and Where to Find Them (2025.acl-long)
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| Challenge: | generative large language models produce hallucinations that are not aligned with world knowledge or input context. |
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Towards Mitigating Hallucinations in Large Vision-Language Models by Refining Textual Embeddings (2026.findings-acl)
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Aakriti Agrawal, Gouthaman KV, Rohith Aralikatti, Gauri Jagatap, Jiaxin Yuan, Sarvesh Baskar, Vijay Kamarshi, Andrea Fanelli, Furong Huang
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MHALO: Evaluating MLLMs as Fine-grained Hallucination Detectors (2025.findings-acl)
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| Challenge: | Hallucination remains a critical challenge for multimodal large language models, undermining their reliability in real-world applications. |
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HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models (2023.emnlp-main)
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| Challenge: | Existing large language models (LLMs) are prone to generate hallucinations . a recent study shows that LLMs are able to generate content that conflicts with the source or cannot be verified by factual knowledge. |
| Approach: | They propose a framework to evaluate the performance of large language models (LLMs) they propose to use a sample of generated and human-annotated hallucinated samples to evaluate their performance . |
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