---
title: "CCD — paper by Zhang et al. (2026) — summary and ideas"
description: "In an empirical study with LLaVA-Med v1.5 on MIMIC-CXR report generation, they add clinical sections (indication, technique… 107 references."
authors: ["Xi Zhang", "Zaiqiao Meng", "Jake Lever", "Edmond S. L. Ho"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: ACL 2026"
source: "https://aclanthology.org/2026.findings-acl.1755/"
doi: "10.18653/v1/2026.findings-acl.1755"
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/zhang-2026-ccd-mitigating-hallucinations-radiology-mllms/"
---
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# CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding

**Authors:** [Xi Zhang](https://md.research.elamur.ai/free/en/authors/xi-zhang/), [Zaiqiao Meng](https://md.research.elamur.ai/free/en/authors/zaiqiao-meng/), Jake Lever and [Edmond S. L. Ho](https://md.research.elamur.ai/free/en/authors/edmond-s-l-ho/) · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: ACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/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.findings-acl.1755/) · [PDF](https://aclanthology.org/2026.findings-acl.1755.pdf) · [DOI 10.18653/v1/2026.findings-acl.1755](https://doi.org/10.18653/v1/2026.findings-acl.1755) · [OpenAlex](https://openalex.org/W4415332438)

**Citations:** 2 citations (OpenAlex, 2026-10-14)

## Summary
Radiology multimodal LLMs often write findings that the image does not support. The authors call these medical hallucinations. In an empirical study with LLaVA-Med v1.5 on MIMIC-CXR report generation, they add clinical sections (indication, technique, comparison, history) to the prompt. Most lexical and clinical scores get worse. This suggests the models react too strongly to textual context that may be irrelevant or counterfactual.

Their fix is Clinical Contrastive Decoding (CCD), which works only at inference time and needs no training or retrieval. A task-specific radiology expert model, such as a symptom classifier, outputs structured labels and their probabilities. CCD uses these in two contrastive stages that adjust token logits while the report is generated. The labels are written into the prompt as descriptive hints, and the confidence scores perturb decoding. The base MLLM stays unchanged.

Across three datasets and several models, CCD improves radiology report generation and also helps VQA accuracy. On MIMIC-CXR, RadGraph-F1 rises by up to 17%. CCD is a lightweight, privacy-friendly way to combine expert models with generative MLLMs.

## Key points
- The pilot study shows that prompt-induced hallucinations persist even when real clinical sections from MIMIC-CXR are used. The baseline is LLaVA-Med v1.5 with greedy decoding.
- CCD has two stages:
  - Symptom-grounded decoding (SCD) mainly reduces missed findings (false negatives).
  - Expert-informed decoding (ECD) mainly suppresses fabricated findings (false positives).
  - Removing both stages hurts performance most.
- Evaluation combines lexical metrics (ROUGE-L, BLEU, BERTScore) with clinical ones (RadGraph-F1, Temporal-F1, RaTEScore, RadEval-BERT, CheXbert-F1). Tested models include MAIRA-2 and LLaVA-Med.
- Expert ablations:
  - Swapping in MedSigLIP as the expert improves results consistently.
  - Restricting the expert to the top-5 symptoms lowers CheXbert-14 F1 by 1.68.
- The guidance weights α and β give the best RadGraph-F1 at 0.5. Evaluation uses automatic metrics only, with no radiologist assessment.

*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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- [University of Glasgow](https://md.research.elamur.ai/free/en/universities/university-of-glasgow/) — University, Glasgow, United Kingdom · 11 works · 635 citations

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## Cite

Cite as: Xi Zhang, Zaiqiao Meng, Jake Lever and Edmond S. L. Ho. 2026. CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding. Findings of the Association for Computational Linguistics: ACL 2026.

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

```bibtex
@inproceedings{zhang2026ccd,
  title = {{CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding}},
  author = {Xi Zhang, Zaiqiao Meng, Jake Lever and Edmond S. L. Ho},
  booktitle = {Findings of the Association for Computational Linguistics: ACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-acl.1755},
  url = {https://doi.org/10.18653/v1/2026.findings-acl.1755}
}
```

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*Updated 1 November 2026*