mlsanityData to 7 Oct 2026

Quantity Matters: Towards Assessing and Mitigating Number Hallucination in Large Vision-Language Models

arXiv 2403.01373 ↗First posted 3 Mar 2024cs.CLcited by 2

On the map: Multimodal LLMs / vision-language models → Hallucination mitigation in LVLMs

Abstract (arXiv)

Large-scale vision-language models have demonstrated impressive skill in handling tasks that involve both areas. Nevertheless, these models frequently experience significant issues with generating inaccurate information, which is hallucination. In this study, we concentrate on a specific type of hallucination-number hallucination, referring to models incorrectly identifying the number of certain objects in pictures. We perform quantitative evaluations regarding number hallucination, showing it to be critical in major open-source large vision-language models. Furthermore, we utilizes two related tasks to conduct an in-depth analysis of number hallucination, revealing the severe inner and outer inconsistency among all tasks. Based on this examination, we devise a training approach aimed at improving consistency to reduce number hallucinations, which leads to an 8% enhancement in performance over direct finetuning methods. Our code and dataset will be released to the community.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Hallucination mitigation in LVLMs, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 11 of its 17 references that are in the map. First posted on arXiv 3 Mar 2024.

Builds on

11 of 17 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023Mitigating Fine-Grained Hallucination by Fine-Tuning Large Vision-Language Models with Caption Rewrites4 Dec
  2. 2023Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding28 Nov
  3. 2023Mitigating Hallucination in Visual Language Models with Visual Supervision27 Nov
  4. 2023Analyzing and Mitigating Object Hallucination in Large Vision-Language Models1 Oct
  5. 2023Evaluation and Analysis of Hallucination in Large Vision-Language Models29 Aug

Other references · 6

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2023Improved Baselines with Visual Instruction Tuningcited by 4,362
  3. 2018Object Hallucination in Image Captioningcited by 661
  4. 2018TallyQA: Answering Complex Counting Questionscited by 152
  5. 2018Learning to Count Objects in Natural Images for Visual Question Answeringcited by 40
  6. 2015Learning to count with deep object featurescited by 8

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025Evaluating Hallucination in Large Vision-Language Models based on Context-Aware Object Similaritiescited by 7
  2. 2025ChartHal: A Fine-grained Framework Evaluating Hallucination of Large Vision Language Models in Chart Understandingcited by 5

Similar, not cited

Close in wording, no citation either way

  1. 2025A Comprehensive Analysis for Visual Object Hallucination in Large Vision-Language Models
  2. 2023Evaluating Object Hallucination in Large Vision-Language Models
  3. 2024Measuring the Measurers: Quality Evaluation of Hallucination Benchmarks for Large Vision-Language Models
  4. 2024Multi-Object Hallucination in Vision-Language Models
  5. 2024VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models
  6. 2025MIHBench: Benchmarking and Mitigating Multi-Image Hallucinations in Multimodal Large Language Models
  7. 2024H-POPE: Hierarchical Polling-based Probing Evaluation of Hallucinations in Large Vision-Language Models
  8. 2024A Survey on Hallucination in Large Vision-Language Models