---
title: "Attribution-Guided Multi-Object Hallucination and Bias Detection"
description: "The authors propose SHAPLENS, a training-free framework that works across model architectures: Hallucination: a… 41 references."
authors: ["Sirat Samyoun", "Yingtai Xiao", "Jian Du"]
year: 2026
venue: "Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.eacl-long.210/"
doi: "10.18653/v1/2026.eacl-long.210"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/samyoun-2026-attribution-guided-multi-object-hallucination/"
---
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# Attribution-Guided Multi-Object Hallucination and Bias Detection in Vision-Language Models

**Authors:** Sirat Samyoun, Yingtai Xiao and [Jian Du](https://md.research.elamur.ai/free/en/authors/jian-du/) · **Year:** 2026 · **Venue:** [Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/eacl/) ([EACL 2026](https://md.research.elamur.ai/free/en/venues/eacl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.eacl-long.210/) · [PDF](https://aclanthology.org/2026.eacl-long.210.pdf) · [DOI 10.18653/v1/2026.eacl-long.210](https://doi.org/10.18653/v1/2026.eacl-long.210) · [OpenAlex](https://openalex.org/W7140133131)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
Vision-language models often name objects that are not in the image (hallucination). They also over-repeat labels, attributes or shared word roots because language priors outweigh visual evidence (linguistic bias). Both problems worsen in cluttered multi-object scenes. There, similarity scores and averaged attention give fake and real objects nearly the same values. The authors propose SHAPLENS, a training-free framework that works across model architectures:

- **Hallucination:** a LogitLens-inspired analysis picks the most informative layers, which turn out to be mid-to-late ones. Kernel SHAP then estimates how much each patch inside an object's box supports the predicted label. Scores are fused across layers and compared with adaptive thresholds.
- **Bias:** token confidence from the real image is contrasted with a language-only signal from a blank image.

On ADE and COCO with four VLMs, SHAPLENS beats the strongest baselines by 8–12% in accuracy and 10–14% in F1 for hallucination detection. On a curated HQH benchmark it gains up to 6% for bias detection. The method separates hallucination from bias as distinct phenomena and stays stable under image degradation, which makes it useful for reliability auditing.

## Key points
- Evaluated models are LLaVA-7B, LLaVA-13B, Qwen2-VL-2B and LLaMA-3.2-Vision-11B, compared against OPERA, CLIP-based and contextual-embedding baselines.
- The layer study used 100 ADE images, and the layer patterns were verified on COCO. Attribution accuracy for LLaVA-7B peaks around layers 19–21, and the other models show similar mid-layer trends.
- Three bias categories are defined: label repetition (e.g., "goat" generated far beyond the true count), attribute bias (excess colour or shape words) and lexical-root propagation (e.g., many "airport …" compounds).
- Attribute bias is the hardest category, with F1 of roughly 0.26–0.42 for all methods. Label bias is detected most reliably, with F1 near 0.9.
- Robustness was tested under occlusion (10–30%), Gaussian noise, box shifts (10–50%) and half or quarter partial views. Hallucination metrics change by at most about 0.0003, and classification decisions do not flip.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Sirat Samyoun, Yingtai Xiao and Jian Du. 2026. Attribution-Guided Multi-Object Hallucination and Bias Detection in Vision-Language Models. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{samyoun2026attribution,
  title = {{Attribution-Guided Multi-Object Hallucination and Bias Detection in Vision-Language Models}},
  author = {Sirat Samyoun, Yingtai Xiao and Jian Du},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.210},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.210}
}
```

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*Updated 1 November 2026*