---
title: "Knowing When to Abstain — paper by Machcha et al. (2026)"
description: "The authors present MedAbstain, a benchmark and evaluation protocol for… 71.43% precision. 76 references, cited by 4 papers here."
authors: ["Sravanthi Machcha", "Sushrita Yerra", "Sahil Gupta", "Aishwarya Sahoo", "Sharmin Sultana", "Hong Yu", "Zonghai Yao"]
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.291/"
doi: "10.18653/v1/2026.eacl-long.291"
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/machcha-2026-knowing-when-abstain-medical-llms/"
---
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# Knowing When to Abstain: Medical LLMs Under Clinical Uncertainty

**Authors:** Sravanthi Machcha, Sushrita Yerra, Sahil Gupta, Aishwarya Sahoo, Sharmin Sultana, [Hong Yu](https://md.research.elamur.ai/free/en/authors/hong-yu/) and [Zonghai Yao](https://md.research.elamur.ai/free/en/authors/zonghai-yao/) · **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.291/) · [PDF](https://aclanthology.org/2026.eacl-long.291.pdf) · [DOI 10.18653/v1/2026.eacl-long.291](https://doi.org/10.18653/v1/2026.eacl-long.291) · [OpenAlex](https://openalex.org/W7140135941)

**Citations:** 5 citations (OpenAlex, 2026-10-14) · [4](https://md.research.elamur.ai/free/en/papers/machcha-2026-knowing-when-abstain-medical-llms/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Benchmarks for medical LLMs mostly reward correct answers. They rarely check whether a model should have declined to respond. The authors present MedAbstain, a benchmark and evaluation protocol for uncertainty and abstention in medical multiple-choice QA. They argue this discrete-choice format also models action selection in agentic systems.

The benchmark uses MedQA (USMLE) and the private AMBOSS question set, each in several variants:
- the original items (NA);
- items with an abstention choice inserted at a random position (A);
- perturbed items with key clinical context removed, alone or combined with the abstention option.

Uncertainty is measured with conformal prediction, which relies on log-probabilities so that closed-source API models can be included. Open and proprietary models are tested under zero-shot, few-shot and chain-of-thought prompting.

High-accuracy models still often answer when they should hold back. An explicit abstention option raises uncertainty and safe abstention far more than removing information does, though some proprietary models such as GPT-4.1 deviate from this pattern. Larger models and more elaborate prompting help little. The study suggests that abstention design matters more than scale for safer clinical use.

## Key points
- **Data:** MedQA (USMLE) and AMBOSS are used in NA, A, perturbed and perturbed+abstention variants. Uncertainty is quantified via conformal prediction over the fixed answer label space.
- **Perturbation quality:** Human annotators rated the removed context's importance on a 1–3 scale (mean 2.388, median 3). They judged abstention clinically appropriate in 77.55% of cases, rising above 90% when the missing information was rated at least moderately important.
- **Model vs. human judgments:** Against these labels, model abstention reached 71.43% precision but only 13.16% recall. Models seldom abstain wrongly but usually fail to abstain when they should.
- **Scaling and prompting:** Neither model scale nor instruction tuning reliably improves abstention. Chain-of-thought can increase uncertainty without making abstention safer. Combining perturbations with the abstention option gives the strongest effect.
- **Release:** The codebase is released under CC-BY-NC 4.0 at github.com/sravanthi6m/MedAbstain; AMBOSS is excluded. The benchmark is English-only and limited to MCQA, both acknowledged as limitations.

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

## Institutions

- [University of Massachusetts Amherst](https://md.research.elamur.ai/free/en/universities/university-of-massachusetts-amherst/) — University, Amherst Center, United States · 106 works · 66,692 citations
- [University of Massachusetts Lowell](https://md.research.elamur.ai/free/en/universities/university-of-massachusetts-lowell/) — University, Lowell, United States · 15 works · 3,942 citations
- [Center for Health Optimization and Implementation Research](https://md.research.elamur.ai/free/en/labs/center-for-health-optimization-and-implementation-research/) — Research institute, Bedford, United States · 3 works · 14 citations

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## Cite

Cite as: Sravanthi Machcha, Sushrita Yerra, Sahil Gupta, Aishwarya Sahoo, Sharmin Sultana, Hong Yu and Zonghai Yao. 2026. Knowing When to Abstain: Medical LLMs Under Clinical Uncertainty. 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{machcha2026knowing,
  title = {{Knowing When to Abstain: Medical LLMs Under Clinical Uncertainty}},
  author = {Sravanthi Machcha, Sushrita Yerra, Sahil Gupta, Aishwarya Sahoo, Sharmin Sultana, Hong Yu and Zonghai Yao},
  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.291},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.291}
}
```

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*Updated 1 November 2026*