# LLM-based ambiguity detection in natural language instructions for collaborative surgical robots

arXiv: https://arxiv.org/abs/2507.11525 · HTML: https://mlsanity.com/paper/2507.11525/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jul 2025.

## Built on by (most cited first, 3 of 3)

- [Assessing the Value of Visual Input: A Benchmark of Multimodal Large Language Models for Robotic Path Planning](https://mlsanity.com/paper/2507.12391/) (2025)
- [Beyond Single Models: Enhancing LLM Detection of Ambiguity in Requests through Debate](https://mlsanity.com/paper/2507.12370/) (2025)
- [Uncertainty, Vagueness, and Ambiguity in Human-Robot Interaction: Why Conceptualization Matters](https://mlsanity.com/paper/2604.15339/) (2026)

## Abstract (arXiv)

Ambiguity in natural language instructions poses significant risks in safety-critical human-robot interaction, particularly in domains such as surgery. To address this, we propose a framework that uses Large Language Models (LLMs) for ambiguity detection specifically designed for collaborative surgical scenarios. Our method employs an ensemble of LLM evaluators, each configured with distinct prompting techniques to identify linguistic, contextual, procedural, and critical ambiguities. A chain-of-thought evaluator is included to systematically analyze instruction structure for potential issues. Individual evaluator assessments are synthesized through conformal prediction, which yields non-conformity scores based on comparison to a labeled calibration dataset. Evaluating Llama 3.2 11B and Gemma 3 12B, we observed classification accuracy exceeding 60% in differentiating ambiguous from unambiguous surgical instructions. Our approach improves the safety and reliability of human-robot collaboration in surgery by offering a mechanism to identify potentially ambiguous instructions before robot action.