HOLM: Hallucinating Objects with Language Models for Referring Expression Recognition in Partially-Observed Scenes (2022.acl-long)
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| Challenge: | a challenge in building AI systems physically present in the world is partial observability, a problem that exists when the entire state of the environment is not known or available to the system. |
| Approach: | They propose a method to infer object hallucinations for the unobserved part of the environment using large pre-trained language models. |
| Outcome: | The proposed method performs better than state-of-the-art approaches on two datasets for dRER. |
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| Challenge: | LVLMs often mistakenly determine objects as present in images where they do not exist . authors propose a new benchmark to evaluate object hallucinations by removing objects from images and asking the model whether it can still see the removed objects. |
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Perceptual Hallucination in Vision–Language Models: Definition, Analysis and Verification (2026.findings-acl)
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| Challenge: | Recent advances in large language models (LLMs) have dramatically improved text understanding and generation capabilities. |
| Approach: | They define perceptual hallucination as the phenomenon where VLMs generate information as if perceived, despite absent or damaged visual evidence. |
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Plausible May Not Be Faithful: Probing Object Hallucination in Vision-Language Pre-training (2023.eacl-main)
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| Challenge: | Large-scale vision-language pre-trained (VLP) models generate unfaithful or nonsensical texts given the source input, which is called hallucination. |
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Evaluating Object Hallucination in Large Vision-Language Models (2023.emnlp-main)
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| Challenge: | Large vision-language models (LVLMs) suffer from object hallucinations, i.e., they tend to generate objects inconsistent with the target images in the descriptions. |
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HEAL: An Empirical Study on Hallucinations in Embodied Agents Driven by Large Language Models (2025.findings-emnlp)
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Trishna Chakraborty, Udita Ghosh, Xiaopan Zhang, Fahim Faisal Niloy, Yue Dong, Jiachen Li, Amit Roy-Chowdhury, Chengyu Song
| Challenge: | Large language models are increasingly being adopted as the cognitive core of embodied agents. |
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| Outcome: | The proposed model can induce hallucinations up to 40 higher than base prompts . the model fails to resolve scene-task inconsistencies, the study finds . |
Do Language Models Know When They’re Hallucinating References? (2024.findings-eacl)
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| Challenge: | State-of-the-art language models (LMs) are notoriously susceptible to generating hallucinated information. |
| Approach: | They propose to use hallucinated book and article references as "model organism" of hallucinism research . authors propose queries to the language model to identify hallucinous references . |
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Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models (2024.emnlp-main)
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| Challenge: | Existing studies have revealed that Large Vision-Language Models suffer from hallucinations in practice, including object hallucines, spatial hallucinos, attribute hallucinications, etc. |
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Visual Referring Expression Recognition: What Do Systems Actually Learn? (N18-2)
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| Challenge: | Existing systems for referring expression recognition ignore linguistic structure, instead relying on shallow correlations introduced by unintended biases in the data selection and annotation process. |
| Approach: | They propose to use a system trained on the input image without the input referring expression to achieve a precision of 71.2% in top-2 predictions. |
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Diving into Mitigating Hallucinations from a Vision Perspective for Large Vision-Language Models (2025.findings-emnlp)
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| Challenge: | Existing benchmarks focus on coarse-grained hallucination detection and fail to capture hallucinics . vision encoders exhibit unique hallucinian characteristics, but suboptimal of simple feature fusion. |
| Approach: | They propose a visual encoder that employs different training paradigms to instill inductive biases in visual encoded models. |
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ARKitSceneRefer: Text-based Localization of Small Objects in Diverse Real-World 3D Indoor Scenes (2023.findings-emnlp)
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| Challenge: | Existing datasets for 3D referring expression comprehension cover large objects and small objects, such as cooking tools and office supplies. |
| Approach: | They propose a 3D referring expression comprehension dataset that uses 3D scenes to ground text representations onto objects in 3D environments. |
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