ALFA: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning
This work addresses the issue of unreliable LLM question-asking for proactive information-gathering, particularly in clinical reasoning, with incremental improvements through fine-grained attribute alignment.
The paper tackles the problem of large language models (LLMs) failing to ask effective questions under uncertainty, which is critical for decision-making in domains like clinical reasoning, and presents ALFA, a framework that reduces diagnostic errors by 56.6% compared to state-of-the-art instruction-tuned LLMs.
Large language models (LLMs) often fail to ask effective questions under uncertainty, making them unreliable in domains where proactive information-gathering is essential for decision-making. We present ALignment via Fine-grained Attributes, (ALFA) a framework that improves LLM question-asking by (i) decomposing the notion of a "good" question into a set of theory-grounded attributes (e.g., clarity, relevance), (ii) controllably synthesizing attribute-specific question variations, and (iii) aligning models via preference-based optimization to explicitly learn to ask better questions along these fine-grained attributes. Focusing on clinical reasoning as a case study, we introduce the MediQ-AskDocs dataset, composed of 17k real-world clinical interactions augmented with 80k attribute-specific preference pairs of follow-up questions, as well as a novel expert-annotated interactive healthcare QA task to evaluate question-asking abilities. Models aligned with ALFA reduce diagnostic errors by 56.6% on MediQ-AskDocs compared to SoTA instruction-tuned LLMs, with a question-level win-rate of 64.4% and strong generalizability. Our findings suggest that explicitly guiding question-asking with structured, fine-grained attributes offers a scalable path to improve LLMs, especially in expert application domains.