Visual Hallucination: Definition, Quantification, and Prescriptive Remediations
Authors: Vipula Rawte, Amitava Das


Reviewer paper summaries: The paper inspects visual hallucination in VLMs by classifying visual hallucination into 3 degrees of hallucinations: alarming, mild, and low; which then further breaks down into 8 categories: Contextual Guessing (CG), Identity Incongruity (II), Geographic Erratum (GE), Visual Illusion (VI), Gender Anomaly (GA), VLM as Classifier (VC), Wrong Reading (WR), and Numeric Discrepancy (ND). The first 3 categories are considered as alarming, the next 3 categories are considered as mild, and the last 2 categories are low. To analyze visual hallucination in LLMs, the paper introduce a Visual Hallucination Dataset (VHILT) derived from Twitter posts data from New York Times. The visual hallucination is measured through two tasks, i.e., image captioning and VQA, which is then annotated by human to indicate whether the generated captions / answers consist of visual hallucination or not. The paper also introduces a metric for measuring visual hallucination dubbed as Visual Hallucination Vulnerability Index (VHVI), which are weighted averaged occurence count across different degree of  hallucinations. The results suggest Kosmos-2 for image captioning, and Instruct- BLIP and LLaVa for VQA exhibit higher vulnerability to these alarming categories of hallucination. The paper also introduce 3 baselines: meta-learning (MeLa), visual contrastive decoding (VCD), and visual retrieval augmented generation (RAG). The results suggest that MeLa significantly improved caption hallucinations in Kosmos-2 across three classes: Visual Illusion, Geographic Erratum, and Contextual Guessing. While VCD significantly improved performance in two hallucination categories, VLM as classifier and Gender Anomaly. While Visual RAG excelled in the caption hallucination task for Kosmos-2, particularly in Geographic Erratum and Identity Incongruity.
Summary of strengths: The paper introduce a new dataset for visual hallucinatio,: VHILT, and a new visual hallucination metric, VHVI, and proposes 3 baselines for mitigating cisual hallucination which effectively alleviates visual hallucination in VLMs.
Summary of weaknesses: - The paper introduces a framework for categorizing visual hallucinationm nonetheless, the implication of the categorization is rather unclear, and its applicability towards existing visual hallucination datasets such as POPE and NOPE is never explored.
- Section 5 does not cover methods explained in Section 4, which makes Section 5 incomplete and/or the Section 4 rather irrelevant and mislead the overall content of the paper.
- The authors mention that the dataset will be published, nonetheless the dataset is sourced from a New York Times posts in Twitter. According to New York Times' terms of use (https://help.nytimes.com/hc/en-us/articles/115014893428-Terms-of-Service), it seems that derivative work will require a prior written permission from the New York Times Rights and Permissions Department (https://help.nytimes.com/hc/en-us/articles/115014891408-Obtaining-and-using-Times-content). In this case, it is unclear how the dataset will later be published, and whether such a prior permission has been granted for this project which might raise an ethical concern on using and sharing the dataset.
- In general, the paper is rather confusing and hard to follow.
Comments, suggestions and typos: - When there are instances where two or more hallucination categories are present, what will  such cases be categorized into?
- How does the degrees of hallucination is defined for each category?
- What is the value of the δH, δM, and δL used for the experiment? 
- How does the impact of the mitigation techniques evaluated? Does it also involve human annotation? If that so, how can the dataset can be reliably used for benchmarking results from future works?
- The use of transition words is rather confusing, e.g., Line 193, line 289, and line 298.
- There is no reference for supporting the visual RAG
Overall assessment scores: 2