mlsanityData to 14 Oct 2026

Hallucination as an Upper Bound: A New Perspective on Text-to-Image Evaluation

arXiv 2509.21257 ↗First posted 25 Sep 2025cs.CV cs.CLcited by 0

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

Abstract (arXiv)

In language and vision-language models, hallucination is broadly understood as content generated from a model's prior knowledge or biases rather than from the given input. While this phenomenon has been studied in those domains, it has not been clearly framed for text-to-image (T2I) generative models. Existing evaluations mainly focus on alignment, checking whether prompt-specified elements appear, but overlook what the model generates beyond the prompt. We argue for defining hallucination in T2I as bias-driven deviations and propose a taxonomy with three categories: attribute, relation, and object hallucinations. This framing introduces an upper bound for evaluation and surfaces hidden biases, providing a foundation for richer assessment of T2I models.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Hallucination mitigation in LVLMs, one of 16 directions in Multimodal LLMs / vision-language models. Builds on 11 of its 14 references that are in the map. First posted on arXiv 25 Sep 2025.

Builds on

11 of 14 references.

Closest ancestors · 4

Same direction, newest first

  1. 2025A Survey of Multimodal Hallucination Evaluation and Detection25 Jul
  2. 2024THRONE: An Object-based Hallucination Benchmark for the Free-form Generations of Large Vision-Language Models8 May
  3. 2024Hallucination of Multimodal Large Language Models: A Survey29 Apr
  4. 2024A Survey on Hallucination in Large Vision-Language Models1 Feb

Other references · 7

  1. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,601
  2. 2023GenEval: An Object-Focused Framework for Evaluating Text-to-Image Alignmentcited by 972
  3. 2023SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Modelscited by 810
  4. 2023TIFA: Accurate and Interpretable Text-to-Image Faithfulness Evaluation with Question Answeringcited by 428
  5. 2023HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Modelscited by 341
  6. 2024DiaHalu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Modelscited by 15
  7. 2024Evaluating Hallucination in Text-to-Image Diffusion Models with Scene-Graph based Question-Answering Agentcited by 9

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  1. 2026Reducing Object Hallucination in LVLMs via Emphasizing Image-negative Tokens
  2. 2026LLM-as-Judge Framework for Evaluating Tone-Induced Hallucination in Vision-Language Models
  3. 2024H-POPE: Hierarchical Polling-based Probing Evaluation of Hallucinations in Large Vision-Language Models
  4. 2025MIHBench: Benchmarking and Mitigating Multi-Image Hallucinations in Multimodal Large Language Models
  5. 2025Two Causes, Not One: Rethinking Omission and Fabrication Hallucinations in MLLMs
  6. 2024VALOR-EVAL: Holistic Coverage and Faithfulness Evaluation of Large Vision-Language Models
  7. 2025Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models
  8. 2023Evaluating Object Hallucination in Large Vision-Language Models