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+Title: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning
+
+URL Source: https://arxiv.org/html/2502.14860
+
+Published Time: Tue, 12 Aug 2025 01:10:20 GMT
+
+Markdown Content:
+Shuyue Stella Li 1⋆ Jimin Mun 2⋆ Faeze Brahman 3
+
+Pedram Hosseini 4 Bryceton G. Thomas 5 Jessica M. Sin 5 Bing Ren 5
+
+Jonathan S. Ilgen 1 Yulia Tsvetkov 1 Maarten Sap 2
+
+1 University of Washington 2 Carnegie Mellon University 3 Allen Institute for AI 
+
+4 Lavita AI 5 Dartmouth Medicine 
+
+stelli@cs.washington.edu, jmun@andrew.cmu.edu 
+
+![Image 1: [Uncaptioned image]](https://arxiv.org/html/2502.14860v2/figures/github-mark.png)[https://github.com/stellalisy/ALFA](https://github.com/stellalisy/ALFA)
+
+![Image 2: [Uncaptioned image]](https://arxiv.org/html/2502.14860v2/figures/hf-logo.png)[https://huggingface.co/datasets/stellalisy/MediQ_AskDocs](https://huggingface.co/datasets/stellalisy/MediQ_AskDocs_preference)
+
+###### Abstract
+
+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 AL ignment via F ine-grained A ttributes, (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.1 1 1 We release all data, code, and models for further research.
+
+![Image 3: Refer to caption](https://arxiv.org/html/2502.14860v2/x1.png)
+
+Figure 1: Effective information-seeking questions are crucial for clinical reasoning. Alfa-aligned models can ask better questions and lead to more accurate diagnosis.
+
+1 Introduction
+--------------
+
+Interactive language models have demonstrated remarkable capabilities across numerous domains (OpenAI et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib50)), yet _proactive_ interaction abilities in high-stakes scenarios—clinical reasoning, legal analysis, investigative journalism—remains a challenge (Fung et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib16)). A key obstacle is the ability of these models to recognize and anticipate missing or ambiguous information and proactively seek clarification (Li et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib38); Deng et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib11)). In clinical practice, for instance, physicians systematically ask patients questions to rule out or confirm relevant diagnoses (Richardson et al., [1995](https://arxiv.org/html/2502.14860v2#bib.bib62); Proffit, [2013](https://arxiv.org/html/2502.14860v2#bib.bib54)). This iterative, information-seeking behavior is essential for accurate and safe decision-making. Similarly, for large language models (LLMs) to serve as _reliable_ decision-support tools for clinicians, they must learn not only to provide answers, but also to identify when additional information is needed, and to ask follow-up questions that effectively explore and adjust possible hypothesis or reduce uncertainty (Figure[1](https://arxiv.org/html/2502.14860v2#S0.F1 "Figure 1 ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")).
+
+However, there are two main challenges in building LLMs that ask good questions, especially in expert domains. First, defining a “good” question is inherently complex and context-dependent. In general, attributes such as _clarity_, _focus_, and _answerability_ are essential (Heritage & Maynard, [2006](https://arxiv.org/html/2502.14860v2#bib.bib22); Roter & Hall, [1987](https://arxiv.org/html/2502.14860v2#bib.bib65); Freed, [1994](https://arxiv.org/html/2502.14860v2#bib.bib15); Searle, [1969](https://arxiv.org/html/2502.14860v2#bib.bib67)); however, in domain-specific scenarios such as clinical reasoning, additional properties—_medical accuracy_, _diagnostic relevance_, and _mitigating differential diagnosis (DDX) biases_—are necessary for reducing diagnostic uncertainty (Richardson et al., [1995](https://arxiv.org/html/2502.14860v2#bib.bib62); Silverman et al., [2016](https://arxiv.org/html/2502.14860v2#bib.bib71); Heritage, [2010](https://arxiv.org/html/2502.14860v2#bib.bib21); Hall et al., [1995](https://arxiv.org/html/2502.14860v2#bib.bib20); West, [1984](https://arxiv.org/html/2502.14860v2#bib.bib84); Stivers & Majid, [2007](https://arxiv.org/html/2502.14860v2#bib.bib74); Ong et al., [1995](https://arxiv.org/html/2502.14860v2#bib.bib49)). Second, instilling the ability to ask good questions in LLMs is technically non-trivial (Li et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib38); Johri et al., [2025](https://arxiv.org/html/2502.14860v2#bib.bib30); Zhang et al., [2024a](https://arxiv.org/html/2502.14860v2#bib.bib94)). Naïve prompting strategies such as “Ask a follow-up question if needed.” may enhance interactivity but lack a principled foundation for defining a _good_ question. We propose leveraging well-established general and task-specific principles from communication theory and psychology to improve LLMs’ information-seeking abilities.
+
+![Image 4: Refer to caption](https://arxiv.org/html/2502.14860v2/x2.png)
+
+Figure 2: Alfa: decompose, synthesize, align.
+
+Methodologically, we introduce a general recipe to incorporate question-asking abilities into LLMs, focusing on clinical reasoning as a case study. Our method—AL ignment via F ine-grained A ttributes (Alfa)—relies on the idea that question quality can be improved by explicitly training models with data grounded in structured, theoretically motivated attributes. Our recipe proceeds in three steps:
+
+1.   1._Decompose_ the goal of asking “good” questions into structured, grounded attributes. 
+2.   2._Synthesize_ counterfactual data by controllably altering any attribute (e.g.make clearer). 
+3.   3._Align_ models using preference optimization algorithms to integrate the attributes and produce a final policy. 
+
+Since labeled conversational datasets containing follow-up questions along a variety of important attributes are scarce, Alfa exposes models to a much broader range of question-asking behaviors than what can be typically found in the wild, especially in specialized domains like clinical interactions.
+
+We instantiate the above recipe with a focus on clinical reasoning, where question-asking is central to reducing diagnostic uncertainty and preventing errors. To this end, we construct a novel dataset, _MediQ-AskDocs_, containing 17k clinical interactions with follow-up questions from the r/AskDocs subreddit 2 2 2 With medical experts verified [following subreddit policy](https://www.reddit.com/r/AskDocs/). See §[Limitations](https://arxiv.org/html/2502.14860v2#Sx1 "Limitations ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning") for further discussion on setting., paired with 80k synthesized counterfactual variants of these questions highlighting each attribute. These counterfactual pairs provide fine-grained training signals for preference learning (Li et al., [2025](https://arxiv.org/html/2502.14860v2#bib.bib39)). Finally, we integrate the attribute-specific signals into a unified policy by combining all synthetic data into a single model, training separate reward models and merging them, or fusing attribute-specific policies. Alfa contrasts with coarse-grained preference learning, offering a more targeted way to refine question-asking behaviors.
+
+As part of _MediQ-AskDocs_, we introduce a novel healthcare QA task of 302 expert-annotated clinical interaction scenarios to evaluate the proposed method. These scenarios are passed into MediQ (Li et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib38)), an interactive clinical simulator, in which the models asks questions to the patient agent. Alfa-aligned models achieve a 64.4% win-rate in question-level evaluation and a 56.6% reduction in diagnostic errors, relative to baselines of SoTA instruction tuned LLMs. Beyond these empirical gains, our work presents a new paradigm for aligning language models to specialized domains by decomposing the complex goal of question-asking into attributes, synthesizing pairwise data in each attribute dimension, and aligning the model to jointly optimize the overall complex goal. This general approach to attribute-based question-asking alignment can be extended to many other domains where systematically eliciting information is key to reliable, effective decision-making.
+
+2 Problem Statement
+-------------------
+
+Aligning LLMs to ask good question requires reasoning along multiple attributes (e.g., accuracy, clarity, focus). However, most alignment paradigms treat these goals as monolithic, aggregating preferences into a single reward that conflates attributes and obscures their individual contributions. We formalize this challenge as follows:
+
+Given a complex goal G G and a dataset with sparse labels 𝒟\mathcal{D}, we aim to learn a policy π\pi that maximizes the composite reward R​(s,a)R(s,a), where:
+
+*   •s s: Current world state (e.g., information acquired so far, conversation history). 
+*   •a a: Next action (e.g., follow-up question asked by the clinician agent π\pi). 
+
+The key challenges lie in the complexity of the goal and the sparsity of labeled data. First, directly optimizing R​(s,a)R(s,a) is infeasible because human preferences for R​(s,a)R(s,a) are noisy and subjective. To address this, Alfa decomposes G G into K K attributes {A 1,…,A k}\{A_{1},\ldots,A_{k}\}, each corresponding to a verifiable criterion with a reward function R k R_{k}. We constrain the selection of A k A_{k} such that each R k​(s,a)R_{k}(s,a) is more measurable compared to R​(s,a)R(s,a).
+
+Second, we cannot observe _parallel_ follow-up questions in natural conversations to construct preference pairs. To this end, we synthesize counterfactual synthetic data 𝒟 s​y​n​t​h k\mathcal{D}_{synth}^{k} for each A k A_{k}, where
+
+𝒟 s​y​n​t​h k={(a i k+,a i k−)|R k​(a i k+)>R k​(a i k−)}.\mathcal{D}_{synth}^{k}=\{(a_{i}^{k+},a_{i}^{k-})|R_{k}(a_{i}^{k+})>R_{k}(a_{i}^{k-})\}.\vskip-2.84526pt
+
+Lastly, Alfa uses a reward integration strategy f f, where R​(s,a)=f​(R 1​(s,a),…,R K​(s,a))R(s,a)=f(R_{1}(s,a),\ldots,R_{K}(s,a)), to combine {R 1,…,R K}\{R_{1},\ldots,R_{K}\} into a policy π\pi, such that:
+
+π∗=arg⁡max π⁡𝔼(s,a)∼π​[f​(R 1,…,R K)​(s,a)],\vskip-2.84526pt\pi^{*}=\arg\max_{\pi}\mathbb{E}_{(s,a)\sim\pi}[f(R_{1},\ldots,R_{K})(s,a)],\vskip-2.84526pt
+
+optimizes performance on the complex goal G G, aligning models to be better question-askers.
+
+3 What Makes a “Good” Question?
+-------------------------------
+
+To systematically improve LLM question asking, we define six key attributes grounded in cognitive science, psychology, and clinical communication research (Heritage & Maynard, [2006](https://arxiv.org/html/2502.14860v2#bib.bib22); Roter & Hall, [1987](https://arxiv.org/html/2502.14860v2#bib.bib65); Chouinard, [2007](https://arxiv.org/html/2502.14860v2#bib.bib8); Freed, [1994](https://arxiv.org/html/2502.14860v2#bib.bib15); Searle, [1969](https://arxiv.org/html/2502.14860v2#bib.bib67); Levinson, [2012](https://arxiv.org/html/2502.14860v2#bib.bib36)). Unlike prior work that relies on implicit heuristics, we explicitly decompose question quality into interpretable, tangible attributes that enhance clinical reasoning.
+
+General Question-Quality Attributes. Effective questions must be clear, targeted, and answerable to drive meaningful interactions. We select three core attributes:
+
+1.   1._Clarity_, aiming to avoid ambiguity and unnecessary complexity (e.g., no jargons), ensuring precise communication (Heritage & Maynard, [2006](https://arxiv.org/html/2502.14860v2#bib.bib22); Roter & Hall, [1987](https://arxiv.org/html/2502.14860v2#bib.bib65); Burns et al., [2022](https://arxiv.org/html/2502.14860v2#bib.bib6)); 
+2.   2._Focus_, directly addressing a specific information gap, yielding more informative responses (Ronfard et al., [2018](https://arxiv.org/html/2502.14860v2#bib.bib64); Gopnik & Wellman, [2012](https://arxiv.org/html/2502.14860v2#bib.bib18); Chouinard, [2007](https://arxiv.org/html/2502.14860v2#bib.bib8); Freed, [1994](https://arxiv.org/html/2502.14860v2#bib.bib15)). E.g., “Has anyone in your family had breast cancer?” is superior to “Has anyone in your family been sick?”; and 
+3.   3._Answerability_, ensuring the question is both within the respondent’s knowledge domain and appropriate for them to answer (e.g., asking about their symptoms and experiences rather than expecting them to provide medical diagnoses or knowledge that falls within the clinician’s expertise)(Levinson, [2012](https://arxiv.org/html/2502.14860v2#bib.bib36); Keil et al., [2008](https://arxiv.org/html/2502.14860v2#bib.bib32); Searle, [1969](https://arxiv.org/html/2502.14860v2#bib.bib67)). 
+
+Domain-specific question-asking attributes. In clinical reasoning, question-asking is a structured diagnostic skill. Drawing from clinical communication research (Richardson et al., [1995](https://arxiv.org/html/2502.14860v2#bib.bib62); Silverman et al., [2016](https://arxiv.org/html/2502.14860v2#bib.bib71); Heritage, [2010](https://arxiv.org/html/2502.14860v2#bib.bib21); Hall et al., [1995](https://arxiv.org/html/2502.14860v2#bib.bib20); West, [1984](https://arxiv.org/html/2502.14860v2#bib.bib84); Stivers & Majid, [2007](https://arxiv.org/html/2502.14860v2#bib.bib74); Ong et al., [1995](https://arxiv.org/html/2502.14860v2#bib.bib49); Proffit, [2013](https://arxiv.org/html/2502.14860v2#bib.bib54)), we define three additional attributes essential for clinical decision-making:
+
+1.   4._Medical Accuracy_ requires alignment with established medical textbook knowledge & guidelines; 
+2.   5._Diagnostic Relevance_ probes for symptoms, risk factors, or contextual details essential to refining differential diagnoses (DDX); and 
+3.   6._Avoiding DDX Bias_ prevents suggestive or leading wording that could introduce cognitive biases and misguide diagnostic reasoning. 
+
+These six attributes form the foundation of Alfa, guiding question optimization to improve LLM reliability in interactive clinical reasoning.
+
+4 Alfa Framework Overview
+-------------------------
+
+We now introduce Alfa, a structured recipe that decomposes the overall question-asking objective (§[4.1](https://arxiv.org/html/2502.14860v2#S4.SS1 "4.1 Grounded Attribute Decomposition ‣ 4 Alfa Framework Overview ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")), generates attribute-specific preference data (§[4.2](https://arxiv.org/html/2502.14860v2#S4.SS2 "4.2 Attribute-Specific Data Generation ‣ 4 Alfa Framework Overview ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")), and trains a policy that integrates the attributes (§[4.3](https://arxiv.org/html/2502.14860v2#S4.SS3 "4.3 Attribute Integration Strategies ‣ 4 Alfa Framework Overview ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")) to ask better follow-up questions.
+
+### 4.1 Grounded Attribute Decomposition
+
+We first decompose the concept of “good” clinical questions into the six attributes A k A_{k} identified in §[3](https://arxiv.org/html/2502.14860v2#S3 "3 What Makes a “Good” Question? ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning") rather than relying on implicit heuristics or coarse scoring. This decomposition enables two advantages: (1) _attribute-specific training signals_ that isolate distinct aspects of question quality, and (2) a _controlled preference structure_ for fine-grained alignment, guiding the next stages of data generation and model alignment.
+
+### 4.2 Attribute-Specific Data Generation
+
+Real-world clinical datasets are scarce, private, and rarely contain annotations distinguishing, for instance, clear vs.ambiguous questions (Mireshghallah et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib42); Ramesh et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib59)). Therefore, we generate _synthetic preference data_ by (1) collecting authentic clinical posts (see §[5.3](https://arxiv.org/html/2502.14860v2#S5.SS3 "5.3 Evaluation ‣ 5 Experimental Setup ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning") for dataset curation details) and (2)using an LLM to generate counterfactual variants along each attribute.
+
+Counterfactual Perturbation. For each question a i a_{i} in the dataset, we prompt an LLM to create “enhanced” and “corrupted” variants (a i k+,a i k−)(a_{i}^{k+},a_{i}^{k-}) that explicitly alter only one attribute k k at a time (e.g., rewriting the original question to be more clear/ambiguous) while keeping others consistent. This enables us to create controlled preference pairs, where one version of a question a i k+a_{i}^{k+} is better aligned with a specific attribute than the other, a i k−a_{i}^{k-}.
+
+Verification & Filtering. We use an LLM-judge(Zheng et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib97)) to verify that the generated perturbations reflect their intended modification. With original question a i a_{i}, we obtain an enhanced question a i k+a_{i}^{k+} and a corrupted question a i k−a_{i}^{k-}, resulting in three preference pairs: (a i k+,a i)(a_{i}^{k+},a_{i}), (a i k+,a i k−)(a_{i}^{k+},a_{i}^{k-}), and (a i,a i k−)(a_{i},a_{i}^{k-}). Given each pair, we provide additional context 3 3 3 Parsed conversation conclusions from future turns. to the LLM-judge, and ask the judge to compare the pairs in the specified attribute dimension (e.g., which question is clearer). If the judge’s decision matches the intended perturbation direction—verifying that R k​(a i k+)>R k​(a i k−)R_{k}(a_{i}^{k+})>R_{k}(a_{i}^{k-})—we retain the sample; otherwise, we discard it. This filtering step removes inconsistencies and ensures that our synthetic preference data provides reliable supervision for model alignment. See details in Appendix[C.1](https://arxiv.org/html/2502.14860v2#A3.SS1 "C.1 Counterfactual Perturbations ‣ Appendix C Implementation Details ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning").
+
+### 4.3 Attribute Integration Strategies
+
+We now _combine_ signals from the attribute-specific data so that the model produces questions that optimize for all attributes. Standard preference optimization algorithms DPO (Rafailov et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib56)) and PPO (Schulman et al., [2017](https://arxiv.org/html/2502.14860v2#bib.bib66)) allow distinct _Points of Integration_ (POI) as described below 4 4 4 While reward sum, which trains attribute-specific reward models then combines the reward scores (e.g., via a learned linear combination or average) to produce a final scalar reward (Wu et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib87); Wang et al., [2024c](https://arxiv.org/html/2502.14860v2#bib.bib82)), remains an option, we do not utilize this strategy due to high compute cost of loading all reward models during PPO without substantial performance improvements (Rame et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib57); Shi et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib70)).:
+
+1.   (1)Data Mixing pools all data into one training set and uses standard DPO/PPO. This treats each attribute-specific comparison as part of a larger set of “better vs.worse” pairs, enabling a single policy to learn from all attributes at once (Wang et al., [2023b](https://arxiv.org/html/2502.14860v2#bib.bib81); Lambert et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib35)). 
+2.   (2)Reward Fusion trains separate reward models (RM), then averages the RM _weights_ in an overall RM which can then be used in PPO to align a final policy (Ramé et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib58)). 
+3.   (3)Policy Fusion trains separate policies or preference models, each specialized for one attribute, and then combine the model weights by averaging or taking a linear combination (Jang et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib25)). This strategy offers the highest degree of parallelism and maximally preserves attribute strengths and interpretability. 
+
+Comparing these integration strategies informs how best to reconcile multiple, sometimes competing, objectives (e.g., _focus_ vs._avoiding DDX bias_) and thereby produce consistently high-quality diagnostic questioning.
+
+5 Experimental Setup
+--------------------
+
+### 5.1 _MediQ-AskDocs_ Dataset Curation
+
+We obtain data from r/AskDocs, a public online health forum, and filter for conversation threads where (i) the patient posts a health inquiry and engage with another user to discuss the issue, and (ii) another user _asks follow-up questions_ to acquire more information. The resulting dataset contains 13,496 unique posts, 17,425 threads, and 24,263 questions. For creating the counterfactual perturbations (§[4.2](https://arxiv.org/html/2502.14860v2#S4.SS2 "4.2 Attribute-Specific Data Generation ‣ 4 Alfa Framework Overview ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")), we sample 4463/433/620 questions for the train/dev/test splits. More details on dataset curation and sampling are in Appendix[B](https://arxiv.org/html/2502.14860v2#A2 "Appendix B Dataset Curation ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning").
+
+### 5.2 Experiments
+
+With the goal of instilling question-asking ability in LLMs in the clinical reasoning domain, we structure our experiments to progressively address two key questions: _(1)Does the entire Alfa pipeline improve clinical question-asking performance?_ and _(2)To what extent is every component of Alfa necessary for improving performance?_ Accordingly, we organize our evaluation as follows:
+
+1. Overall Performance. We first confirm that Alfa meaningfully reduces diagnostic errors and elicits better questions in an interactive clinical scenario (§[6.1](https://arxiv.org/html/2502.14860v2#S6.SS1 "6.1 Alfa Improves Overall Question Asking ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")).
+
+2. Key Pipeline Components. We isolate the two core components of Alfa:
+
+*   •We compare the _decomposed_ attributes (e.g., clarity, answerability) to a “coarse” attribute (simply “good or bad”) to examine the effect of theory-grounded decomposition (§[6.2](https://arxiv.org/html/2502.14860v2#S6.SS2 "6.2 Fine-Grained Attributes Outperform Coarse Attribute ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")). 
+*   •We compare preference tuning to supervised fine-tuning (SFT) on _the same_ synthetic data, revealing how pairwise reward signals refine question-asking beyond what SFT alone can achieve (§[6.3](https://arxiv.org/html/2502.14860v2#S6.SS3 "6.3 Preference Tuning Outperforms SFT ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")). 
+
+3. Ablation Studies. We dissect each design choice to identify its role in the observed improvements. We compare _attribute-integration strategies_, apply _quality filter_ on the synthetic data, examine _data perturbation directions_ (corruption vs.enhancement), ablate _individual attributes_, and test _out-of-distribution generalization_ on a separate diagnostic task 5 5 5 All ablations are done with DPO due to lighter compute requirements unless otherwise specified. (§[6.4](https://arxiv.org/html/2502.14860v2#S6.SS4 "6.4 Ablation Studies ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")).
+
+Collectively, these analyses elucidate how components in Alfa—attribute decomposition, data synthesis, preference tuning—work together to improve question-asking in high-stakes clinical contexts. See implementation details in Appendix[C](https://arxiv.org/html/2502.14860v2#A3 "Appendix C Implementation Details ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning").
+
+### 5.3 Evaluation
+
+We evaluate the aligned models along two fronts: Direct Question Quality, measured through expert human annotations and automatic LLM-judge comparison for the overall question quality, and Clinical Decision Impact, quantified by how well the model questions help reduce diagnostic errors.
+
+#### Direct win-rate with LLM-judge.
+
+We create an LLM-judge to compare pairs of questions, adopting prompt structures from Li et al. ([2023](https://arxiv.org/html/2502.14860v2#bib.bib40)) and Dubois et al. ([2023](https://arxiv.org/html/2502.14860v2#bib.bib13)). Specifically, we measure the percentage of times our aligned models’ questions are preferred over those of the baseline instruction-tuned models (llama-3.2-3b-Instruct and llama-3.1-8b-Instruct) by the judge and report win-rate. Each comparison is carried out by gpt-4o with rationales, permuting the presentation order in each pair to mitigate ordering bias. As an validity check, the LLM-judge assigns a higher win-rate to questions from verified experts than those from non-experts (Appendix[D](https://arxiv.org/html/2502.14860v2#A4 "Appendix D Expert vs. Non-Expert Human-Written Questions ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")), suggesting our evaluation aligns with domain expertise.
+
+#### Expert manual evaluation.
+
+To assess the quality of generated questions and further validate the LLM-judge, we conduct manual preference rankings with _three medical experts_ from our research team and compute win-rate of select models based on majority vote. See Appendix[F](https://arxiv.org/html/2502.14860v2#A6 "Appendix F Expert Manual Evaluation ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning") for further details.
+
+#### Interactive diagnostic accuracy.
+
+We use the MediQ interactive framework (Li et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib38))—patient-clinician simulator—to holistically evaluate Alfa in a more realistic setting. MediQ presents some initial information x 0 x^{0} (often the patient’s chief complaint), a medical inquiry κ\kappa, and tests an expert agent’s ability to ask follow-up questions a a to the patient until it has enough information to make a diagnosis y y. We replace the question generator module in the MediQ expert agent with models trained with Alfa, while keeping all other modules (patient system and diagnosis generator) consistent. We quantify the utility of the question generator with the accuracy of the diagnosis y y.
+
+#### The _MediQ-AskDocs_ task.
+
+MediQ is compatible with any QA task with contextual information. We introduce a novel healthcare QA task as part of the _MediQ-AskDocs_ dataset: 302 consumer healthcare multiple choice questions manually annotated by medical experts. The task is automatically generated by o1 using the test split of _MediQ-AskDocs_ (§[5.1](https://arxiv.org/html/2502.14860v2#S5.SS1 "5.1 MediQ-AskDocs Dataset Curation ‣ 5 Experimental Setup ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")) and achieves 85.9% agreement with majority voted manual annotations from medical experts. See Appendix[E](https://arxiv.org/html/2502.14860v2#A5 "Appendix E MediQ-AskDocs Task Construction ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning") for task construction and annotation details. Additionally, we use MedQA (Jin et al., [2020](https://arxiv.org/html/2502.14860v2#bib.bib27)) with MediQ to examine the models’ ability to generalize out-of-domain.
+
+6 Results & Analysis
+--------------------
+
+### 6.1 Alfa Improves Overall Question Asking
+
+We begin by comparing Alfa with two baselines: (1) the base instruction-tuned models (llama-3.2-3B-Instruct and llama-3.1-8B-Instruct) and (2) models trained via supervised fine-tuning (SFT) on the human-written questions. The Policy Fusion attribute integration strategy (§[4.3](https://arxiv.org/html/2502.14860v2#S4.SS3 "4.3 Attribute Integration Strategies ‣ 4 Alfa Framework Overview ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")) is reported for both Alfa-DPO and Alfa-PPO in Table[1](https://arxiv.org/html/2502.14860v2#S6.T1 "Table 1 ‣ 6.1 Alfa Improves Overall Question Asking ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning").
+
+Our results confirm that fine-tuning the human-written questions in _MediQ-AskDocs_ (SFT) already outperforms base models, establishing usefulness of the dataset. Notably, superior performance of the Alfa-aligned models shows _explicitly modeling structured, theory-grounded attributes substantially boosts both question quality and diagnostic accuracy_, reducing diagnostic errors by 56.62% (increasing accuracy by 21.5%).
+
+Table 1: Main results. Alfa models consistently outperform base instruct and SFT models. Note that win-rates for base models are set to 50% to represent equal preference when comparing against themselves.
+
+Table 2: Fine-grained (Alfa) vs. Coarse Attributes. Fine-grained attributes lead to better downstream diagnostic accuracy and similar win-rates compared to coarse-grained objective. Expert evaluation for 3B coarse vs.fine-grained shows identical win-rate against the base model: 59.4%.
+
+### 6.2 Fine-Grained Attributes Outperform Coarse Attribute
+
+The core idea of Alfa is to decompose a complex goal into structured, theory-grounded attributes rather than treating it as one coarse objective. We examine the role of attribute decomposition by comparing alignment with fine-grained attributes (Alfa) with a simpler approach that optimizes models on a coarse “better” vs.“worse” distinction (Geng et al., [2025](https://arxiv.org/html/2502.14860v2#bib.bib17)). Both models undergo the same counterfactual data generation and alignment processes, but the coarse model lacks explicit attribute separation.
+
+![Image 5: Refer to caption](https://arxiv.org/html/2502.14860v2/x3.png)
+
+Figure 3: Qualitative example contrasting models aligned with fine-grained and coarse attributes. Alfa model tends to ask more logical questions. 
+
+Results in Table[2](https://arxiv.org/html/2502.14860v2#S6.T2 "Table 2 ‣ 6.1 Alfa Improves Overall Question Asking ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning") show that Alfa, trained with fine-grained attributes, achieves higher performance compared to models trained on coarse attributes on MediQ diagnostic accuracy, while the two methods show comparable LLM-judge win-rates. Aligning models based on structured attributes guides the reasoning process in a theory-grounded way, resulting in substantially better downstream performance. Additionally, qualitative analysis (Figure [3](https://arxiv.org/html/2502.14860v2#S6.F3 "Figure 3 ‣ 6.2 Fine-Grained Attributes Outperform Coarse Attribute ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")) shows that the coarse-aligned model tends to ask more superficial questions such as waiting for 8 weeks and check back, while the Alfa-model’s question, "Was this the only issue that was found?", indicates some form of reasoning to rule out other factors that might have contributed to the patient’s concern. Further, coarsely aligned models generalize poorly to out-of-distribution tasks (§[6.4](https://arxiv.org/html/2502.14860v2#S6.SS4.SSS0.Px5 "V. Alfa Models Robustly Generalize to Out-of-Distribution Settings. ‣ 6.4 Ablation Studies ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")).
+
+### 6.3 Preference Tuning Outperforms SFT
+
+Table 3: Preference tuning is crucial in improving model performance. Supervised fine-tuning on the same synthetic data does not show as much performance gain.
+
+Another core advantage of Alfa is its use of pairwise preference learning, which allows models to refine question-asking beyond what supervised fine-tuning (SFT) achieves. To test whether Alfa’s improvements come solely from exposure to diverse synthetic data or from learning structured preferences, we compare Alfa with models fine-tuned on real data (SFT-Real), synthetic enhanced data (SFT-Synthetic), and both (SFT-Combined). 
+
+As shown in Table[3](https://arxiv.org/html/2502.14860v2#S6.T3 "Table 3 ‣ 6.3 Preference Tuning Outperforms SFT ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning"), Alfa outperforms SFT-Combined despite learning from the same data, confirming that learning directional differences from pairwise comparisons is key to better question-asking. These results highlight pairwise contrastive optimization as a necessary step for models to learn how to ask better follow-up questions.
+
+*PO POI Size Win-rate MediQ-AD Win-rate (human)
+DPO Data 3B 68.55 85.01 52.00
+8B 68.23 88.74—
+Policy 3B 64.97 87.75 41.00
+8B 65.13 88.08—
+PPO Data 3B 97.34 84.77 75.00
+Reward 3B 97.98 84.44 74.00
+Policy 3B 64.84 86.75 40.00
+
+Table 4: Attribute integration strategies. 
+
+Table 5: Synthetic data quality. Filtering slightly improves diagnostic accuracy.
+
+![Image 6: [Uncaptioned image]](https://arxiv.org/html/2502.14860v2/x4.png)
+
+Figure 4: All synthetic data directions are helpful. Including corruptions, enhancements, and the original data shows the best performance.
+
+### 6.4 Ablation Studies
+
+#### I. When to integrate the attributes?
+
+We now examine the effect of various attribute integration strategies from §[4.3](https://arxiv.org/html/2502.14860v2#S4.SS3 "4.3 Attribute Integration Strategies ‣ 4 Alfa Framework Overview ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning"): data-mixing, reward-fusion (PPO only), and policy-fusion. Table[4](https://arxiv.org/html/2502.14860v2#S6.T4 "Table 4 ‣ 6.3 Preference Tuning Outperforms SFT ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning") reveals three key findings. First, data-mixing achieves higher question win-rate but yields lower diagnostic accuracy than policy-fusion, and reward-fusion in PPO mirrors data-mixing patterns. This suggests that _greater attribute separation leads to improved performance_, consistent with prior observations in Yang et al. ([2025](https://arxiv.org/html/2502.14860v2#bib.bib91)). Second, The LLM-judge scores had substantial agreement with human expert assessments for strategies with higher win-rate (Gwet’s AC1 score of .68 and .72 each for Alfa-PPO-Reward and Alfa-PPO-Data), establishing the LLM-judge as a reliable proxy for human assessment in this simulated interaction environment. The high LLM-judge score of Alfa-PPO-Reward and Alfa-PPO-Data are echoed in the human evaluation with the highest win-rates of 74% and 75% respectively. See Appendix[F](https://arxiv.org/html/2502.14860v2#A6 "Appendix F Expert Manual Evaluation ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning") and [G](https://arxiv.org/html/2502.14860v2#A7 "Appendix G Qualitative Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning") for further details on annotations and qualitative analysis.
+
+#### II. Gains from Synthetic Data Quality.
+
+Alfa relies on synthetic question perturbations to expose models to counterfactual scenarios. To ensure data quality, we filtered out 13.9% of generated pairs where LLM-judge ratings misalign with intended perturbation directions (§[4.2](https://arxiv.org/html/2502.14860v2#S4.SS2 "4.2 Attribute-Specific Data Generation ‣ 4 Alfa Framework Overview ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")). Filtering slightly improves both question quality and diagnostic accuracy, emphasizing the value of high-quality synthetic data (Table[5](https://arxiv.org/html/2502.14860v2#S6.T5 "Table 5 ‣ 6.3 Preference Tuning Outperforms SFT ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")).
+
+#### III. Synthetic Corruption vs. Enhancement.
+
+In the counterfactual pairwise data generation stage of Alfa, each original sample a a is synthesized in two directions along each attribute dimension (e.g.more relevant and less relevant) to get a+a^{+} and a−a^{-}. In this section, we examine how the generation direction—_enhanced_ (“more X”) vs. _corrupted_ (“less X”)—influence performance. Specifically, we compare models trained with corruption only pairs (a,a−)(a,a^{-}), enhancement only pairs (a+,a)(a^{+},a), synthetic corruption and enhancement pairs (a+,a−)(a^{+},a^{-}), and all of the above. In Figure[4](https://arxiv.org/html/2502.14860v2#S6.F4 "Figure 4 ‣ 6.3 Preference Tuning Outperforms SFT ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning"), we find while all directions are beneficial, combining all three pairs brings the most gains, especially apparent in the smaller 3B model.
+
+Table 6: Policy Fusion DPO w/ attribute groups.
+
+![Image 7: [Uncaptioned image]](https://arxiv.org/html/2502.14860v2/x5.png)
+
+Figure 5: Models aligned with general vs.clinical attributes show distinct behaviors. Alfa-General: clear and focused, but less relevant and contains DDX bias ("mono"); Alfa-Clinical: professional but uses medical terms hindering answerability.
+
+#### IV. Attribute-Specific Influences.
+
+A key component of Alfa is the explicit decomposition of question-asking into fine-grained, theory-grounded attributes. To assess their individual contributions, we conduct an ablation study where we remove one attribute at a time and evaluate the model’s performance. We also compare general question-asking attributes (clarity, focus, answerability) with clinical attributes (medical accuracy, diagnostic relevance, avoiding DDX bias).
+
+Table[6](https://arxiv.org/html/2502.14860v2#S6.T6 "Table 6 ‣ III. Synthetic Corruption vs. Enhancement. ‣ 6.4 Ablation Studies ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning") shows that removing any attribute leads to performance drops, confirming their importance in clinical question-asking. Clinical attributes have a stronger impact on MediQ accuracy.  Avoiding DDX bias—factors such as premature closure and availability bias leading to incomplete/incorrect differential diagnoses—is the most critical,  suggesting that models need explicit training to counteract cognitive biases. Qualitatively, the questions generated by models aligned with general attributes vs. clinical domain-specific attributes show distinct styles (Figure[5](https://arxiv.org/html/2502.14860v2#S6.F5 "Figure 5 ‣ III. Synthetic Corruption vs. Enhancement. ‣ 6.4 Ablation Studies ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")), highlighting the impact of feature selection. These results validate Alfa’s structured attribute alignment, demonstrating that both domain-specific capabilities and general question quality contribute to effective clinical decision-making.
+
+#### V. Alfa Models Robustly Generalize to Out-of-Distribution Settings.
+
+We further assess Alfa’s ability to generalize beyond the _MediQ-AskDocs_ task by evaluating on a more challenging clinical reasoning benchmark _unseen_ during training, MedQA (Jin et al., [2021](https://arxiv.org/html/2502.14860v2#bib.bib28)), in the same MediQ-style interactive setting. Across intergration strategies, Alfa-aligned models generally outperform or match the coarse-alignment baseline when moving to MedQA (Figure[6](https://arxiv.org/html/2502.14860v2#S6.F6 "Figure 6 ‣ VI. Alfa Outperforms State-of-the-art General & Medical LLMs ‣ 6.4 Ablation Studies ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning")). These suggests that learning from structured, theory-grounded attributes can enhance an LLM’s robustness in new and more diverse clinical settings, highlighting Alfa’s potential for broader applicability in real-world clinical scenarios.
+
+#### VI. Alfa Outperforms State-of-the-art General & Medical LLMs
+
+Lastly, we compare Alfa-aligned models to both closed-source general purpose models and medical-specific models by plugging these models into MediQ as the question-generator. Table [7](https://arxiv.org/html/2502.14860v2#S6.T7 "Table 7 ‣ VI. Alfa Outperforms State-of-the-art General & Medical LLMs ‣ 6.4 Ablation Studies ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning") shows that Alfa shows superior downstream task utility, substantially outperforming even much larger SOTA (general and medical) LLMs.
+
+![Image 8: [Uncaptioned image]](https://arxiv.org/html/2502.14860v2/x6.png)
+
+Figure 6: 3B Model performance on the interactive MediQ-MedQA task. Models aligned with Alfa are more robust to out-of-distribution data.
+
+Table 7: Diagnostic Accuracy of Various Models
+
+7 Related Work
+--------------
+
+LLMs have the potential to significantly transform medicine by enhancing personalized care and accessibility (Shanmugam et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib69)). Models trained with medical data contain rich medical knowledge (Singhal et al., [2025](https://arxiv.org/html/2502.14860v2#bib.bib73); Lewis et al., [2020](https://arxiv.org/html/2502.14860v2#bib.bib37); Chen et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib7); Labrak et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib34); Singhal et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib72); Brin et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib5)); however, systematic evaluations reveal persistent weaknesses in instruction-following, multi-hop reasoning, and the nuanced pragmatics that arise in real clinical encounters (Hager et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib19); Arroyo et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib2); Nov et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib47); Zhang et al., [2014](https://arxiv.org/html/2502.14860v2#bib.bib96)). These shortcomings are partly masked by the dominance of static, single-turn medical QA benchmarks, on which current models already achieve near-saturated scores (Jin et al., [2020](https://arxiv.org/html/2502.14860v2#bib.bib27); Pal et al., [2022](https://arxiv.org/html/2502.14860v2#bib.bib52)). Recent work has moved away from the static single-turn paradigm and highlight _proactive information-seeking_ as a prerequisite to reliable and effective clinical reasoning (Li et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib38); Hu et al., [2024b](https://arxiv.org/html/2502.14860v2#bib.bib24)).  CoAD (Wang et al., [2023a](https://arxiv.org/html/2502.14860v2#bib.bib79)) is an early exploration toward interactivity, yet it operates in a markedly simplified symbolic environment: the agent selects from a small closed set of symptoms and diseases under dense supervision—impossible to obtain in real-life interactions. Crucially, the setting eliminates the linguistic realization entirely as the “question” is an index to a symptom list. Alfa tackles the more demanding challenge in the open-text regime: teaching an LLM to decide simultaneously what information remains most diagnostically valuable and how to ask for it in a way that is bias-sensitive and pragmatically appropriate.
+
+Methodologically, our approach builds on recent data-centric alignment techniques that create synthetic preference signals for alignment (Li et al., [2025](https://arxiv.org/html/2502.14860v2#bib.bib39); Mishra et al., [2024a](https://arxiv.org/html/2502.14860v2#bib.bib43); Ding et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib12); Park et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib53)). Inspired by prior work on multi-objective RLHF (Zhou et al., [2023b](https://arxiv.org/html/2502.14860v2#bib.bib99); Wu et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib87)), we extend PPO (Ouyang et al., [2022](https://arxiv.org/html/2502.14860v2#bib.bib51); Christiano et al., [2017](https://arxiv.org/html/2502.14860v2#bib.bib9)) and DPO (Rafailov et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib55)) to align models with attribute-specific datasets, and uniquely compare different integration points of the fine-grained preference signals (Rame et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib57); Chronopoulou et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib10); Wang et al., [2024a](https://arxiv.org/html/2502.14860v2#bib.bib78)).
+
+8 Discussion
+------------
+
+Effective question-asking is a fundamental yet underdeveloped capability in large language models, particularly in high-stakes domains like clinical reasoning. We proposed Alfa, a framework that explicitly teaches models to ask better questions by decomposing question quality into theory-grounded, fine-grained attributes and aligning them through preference-based optimization, rather than treating such nuanced and complex goal as a monolithic objective. We introduced _MediQ-AskDocs_, a comprehensive dataset of training data, preference data, and a healthcare QA task, showing that models trained with Alfa substantially outperform baselines. While focused on medicine as a case study, Alfa is a general recipe adaptable to any field where clear, targeted questioning is essential, paving the way for interactive and reliable systems.
+
+#### Future Directions.
+
+While Alfa demonstrates significant improvements in clinical question-asking within controlled scenarios, several important directions warrant exploration. First, incorporating contextual factors that shape real clinical reasoning—such as care setting constraints (rural vs. urban hospitals), available diagnostic resources, and physician-patient relationship history—could enhance the framework’s real-world applicability. Future work could explore dynamic weighting mechanisms conditioned on these contexts to improve attribute integration. Additionally, integrating multimodal inputs beyond text, such as tone analysis, non-verbal cues, and existing electronic health record data, could better mirror human clinical interactions. The Alfa framework could also benefit from incorporating collaborative decision-making elements, where models learn to ask patients about their own hypotheses or concerns, and to leverage input from healthcare team members. Finally, extending Alfa to other high-stakes domains requiring systematic information-gathering—such as legal discovery, investigative journalism, or financial risk assessment—could validate its generalizability beyond healthcare while revealing domain-specific attribute requirements.
+
+Limitations
+-----------
+
+Manual attribute selection.Alfa requires manual selection of attributes when adapting to new expert domains. While it offers a structured framework, determining which attributes are essential still depends on domain expertise. However, Alfa can also help evaluate attribute necessity across different fields.
+
+LLM dependence for counterfactual generation. The counterfactual perturbation step relies on LLMs to generate and evaluate counterfactual question variants (specifically, meta-llama/Llama-3.1-405B-Instruct-FP8), assuming they correctly interpret attributes like clarity and relevance. Future work should incorporate human verification of counterfactuals and attribute-level rankings.
+
+Subjectivity in human annotation. Evaluating follow-up questions is inherently subjective and scenario-dependent. Some annotators expressed difficulty in ranking questions, stating that “none of the questions were good” or that “all of them were acceptable.” To ensure annotation quality, we implemented a four-question screening test with known ground-truth answers, filtering out annotators who failed to meet a predefined accuracy threshold. However, this approach does not fully eliminate the risk of variability in domain expertise.
+
+Data and scope. Our dataset is derived from online health forum discussions (r/AskDocs) rather than in-person clinician-patient dialogues in a hospital setting. While this source provides diverse real-world medical inquiries, it does not fully capture the structured questioning strategies used in professional clinical settings. Thus, while Alfa offers a strong technical foundation for studying medical question-asking, it should not be viewed as a direct replacement for physician training or real clinical interactions. Expanding to EHR-based or in-hospital dialogue datasets would improve clinical applicability.
+
+The other presumption underlying this work is that all of these medical problems have verifiable solutions. In actual medical practice—particularly in settings where problems are acute and undefined—it is quite likely that clinicians will come to slightly or entirely different solutions for the same problem (based on their own experiences or expertise).
+
+Ethics Statement
+----------------
+
+Alfa aims to improve LLM-driven question-asking in clinical reasoning, but its development and potential deployment pose potential ethical risks related to misinformation, bias, privacy, and regulatory compliance.
+
+A primary concern is misinformation and overreliance on AI-generated questions. While Alfa improves question quality, it does not provide any sense of guarantee on factuality. If used without human oversight, it could generate misleading, irrelevant, or overly confident questions, potentially influencing clinical decision-making and leading to misdiagnosis or unnecessary medical interventions.
+
+Bias in training data and evaluation is a key risk. Alfa, trained on r/AskDocs, may not represent diverse populations, conditions, or expert strategies, leading to systematic biases that reinforce healthcare disparities. U.S.-based, English-speaking annotators further limit generalizability. Reliance on LLM-judges may introduce automation bias, reinforcing subtle inaccuracies. Future work should expand datasets and evaluation to more diverse populations and implement bias mitigation strategies.
+
+Privacy and data security are additional risks. Although Alfa does not process private medical records, future adaptations using clinical data or electronic health records (EHRs) must protect sensitive patient information. Transparent data governance frameworks and strict access controls are necessary for responsible use in healthcare applications.
+
+Alfa is intended as a technical contribution to the field of computer science rather than a standalone clinical tool. The framework must be integrated with human-in-the-loop supervision, where clinicians retain final decision-making authority. Future work should explore uncertainty calibration, ethical safeguards, and regulatory alignment to ensure safe, fair, and reliable AI-assisted clinical reasoning.
+
+Acknowledgements
+----------------
+
+This research was developed with funding from the Defense Advanced Research Projects Agency’s (DARPA) SciFy program (Agreement No. HR00112520300). The views expressed are those of the author and do not reflect the official policy or position of the Department of Defense or the U.S.Government. This material is based upon work supported by the Defense Advanced Research Projects Agency and the Air Force Research Laboratory, contract number(s): FA8650-23-C-7316. Any opinions, findings and conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of AFRL or DARPA. We would like to thank Dr. Saeed Hassanpour and the Dartmouth Center for Precision Health and Artificial Intelligence (CPHAI) for helping to facilitate our collaboration with the medical professionals team in partnership with Lavita AI.
+
+References
+----------
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+*   Yang et al. (2025) Jinluan Yang, Dingnan Jin, Anke Tang, Li Shen, Didi Zhu, Zhengyu Chen, Daixin Wang, Qing Cui, Zhiqiang Zhang, Jun Zhou, Fei Wu, and Kun Kuang. Mix data or merge models? balancing the helpfulness, honesty, and harmlessness of large language model via model merging, 2025. URL [https://arxiv.org/abs/2502.06876](https://arxiv.org/abs/2502.06876). 
+*   Yao et al. (2024) Zonghai Yao, Aditya Parashar, Huixue Zhou, Won Seok Jang, Feiyun Ouyang, Zhichao Yang, and Hong Yu. Mcqg-srefine: Multiple choice question generation and evaluation with iterative self-critique, correction, and comparison feedback. _arXiv preprint arXiv:2410.13191_, 2024. 
+*   Yao et al. (2025) Zonghai Yao, Aditya Parashar, Huixue Zhou, Won Seok Jang, Feiyun Ouyang, Zhichao Yang, and Hong Yu. Mcqg-srefine: Multiple choice question generation and evaluation with iterative self-critique, correction, and comparison feedback, 2025. URL [https://arxiv.org/abs/2410.13191](https://arxiv.org/abs/2410.13191). 
+*   Zhang et al. (2024a) Michael JQ Zhang, W Bradley Knox, and Eunsol Choi. Modeling future conversation turns to teach llms to ask clarifying questions. _arXiv preprint arXiv:2410.13788_, 2024a. 
+*   Zhang et al. (2024b) Michael JQ Zhang, Zhilin Wang, Jena D. Hwang, Yi Dong, Olivier Delalleau, Yejin Choi, Eunsol Choi, Xiang Ren, and Valentina Pyatkin. Diverging preferences: When do annotators disagree and do models know?, 2024b. URL [https://arxiv.org/abs/2410.14632](https://arxiv.org/abs/2410.14632). 
+*   Zhang et al. (2014) Thomas Zhang, Jason HD Cho, and Chengxiang Zhai. Understanding user intents in online health forums. In _Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics_, pp. 220–229, 2014. 
+*   Zheng et al. (2023) Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric Xing, Hao Zhang, Joseph E Gonzalez, and Ion Stoica. Judging llm-as-a-judge with mt-bench and chatbot arena. In A.Oh, T.Naumann, A.Globerson, K.Saenko, M.Hardt, and S.Levine (eds.), _Advances in Neural Information Processing Systems_, volume 36, pp. 46595–46623. Curran Associates, Inc., 2023. URL [https://proceedings.neurips.cc/paper_files/paper/2023/file/91f18a1287b398d378ef22505bf41832-Paper-Datasets_and_Benchmarks.pdf](https://proceedings.neurips.cc/paper_files/paper/2023/file/91f18a1287b398d378ef22505bf41832-Paper-Datasets_and_Benchmarks.pdf). 
+*   Zhou et al. (2023a) Hongjian Zhou, Fenglin Liu, Boyang Gu, Xinyu Zou, Jinfa Huang, Jinge Wu, Yiru Li, Sam S Chen, Peilin Zhou, Junling Liu, et al. A survey of large language models in medicine: Progress, application, and challenge. _arXiv preprint arXiv:2311.05112_, 2023a. 
+*   Zhou et al. (2023b) Zhanhui Zhou, Jie Liu, Chao Yang, Jing Shao, Yu Liu, Xiangyu Yue, Wanli Ouyang, and Yu Qiao. Beyond one-preference-for-all: Multi-objective direct preference optimization. _arXiv preprint arXiv:2310.03708_, 2023b. 
+
+Appendix A Extended Related Works
+---------------------------------
+
+#### Clinical LLMs.
+
+LLMs have potential to highly impact medicine (Moor et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib45); Thirunavukarasu et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib75)) from personalizing care to improving accessibility (Rodriguez et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib63); August et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib3)). Thus, many language models have focused on clinical knowledge and usage including closed-sourced Med-PaLM 2 (Singhal et al., [2025](https://arxiv.org/html/2502.14860v2#bib.bib73)) to open models such as BioGPT (Lewis et al., [2020](https://arxiv.org/html/2502.14860v2#bib.bib37)), Meditron (Chen et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib7)), and BioMistral (Labrak et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib34)) to name a few (Toma et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib76); Zhou et al., [2023a](https://arxiv.org/html/2502.14860v2#bib.bib98)). More recently, many non-medical, general purpose models such as OpenAI’s o1 have outperformed medically adapted models (Xie et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib88); Jeong et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib26)). These models have shown human-level performance on MedQA (Jin et al., [2020](https://arxiv.org/html/2502.14860v2#bib.bib27)) and other medical knowledge benchmarks (Singhal et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib72)) and some have even shown to provide human-level soft skill such as empathy (Brin et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib5)).
+
+#### Clinical Reasoning and Question-asking of LLMs.
+
+Reasoning abilities of these systems, especially under complex, high-stakes demands of medical interaction fulfilling various intentions of users (Nov et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib47); Zhang et al., [2014](https://arxiv.org/html/2502.14860v2#bib.bib96)) require further attention. More specifically, clinical reasoning requires ability to ask effective questions (Silverman et al., [2016](https://arxiv.org/html/2502.14860v2#bib.bib71)), crucial for information gathering phase with iterative hypotheses testing and updating. Shaikh et al. ([2024](https://arxiv.org/html/2502.14860v2#bib.bib68)) highlighted general lack of question-asking by LLMs in various contexts. While prior works have focused on improving question-asking of LLMs (Andukuri et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib1); Rao & Daumé III, [2018](https://arxiv.org/html/2502.14860v2#bib.bib60)) with some focused diagnostic conversations, these works have been limited to rule-based, toy scenarios (Hu et al., [2024b](https://arxiv.org/html/2502.14860v2#bib.bib24)), or prompting-based techniques (Li et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib38)). Further, ALBA (Varadarajan et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib77)) examined question-asking under mental health assessment setting. Thus, our work expand on such prior works towards a more flexible medical dialogue system with effective question-asking under various real-world user queries.
+
+#### Alignment Methods.
+
+Methodologically, our work adopts various alignment algorithms, especially PPO (Ouyang et al., [2022](https://arxiv.org/html/2502.14860v2#bib.bib51); Christiano et al., [2017](https://arxiv.org/html/2502.14860v2#bib.bib9)) and DPO (Rafailov et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib55)). Moreover, to integrate complex and nuanced preferences, multi-objective settings have been explored including MODPO (Zhou et al., [2023b](https://arxiv.org/html/2502.14860v2#bib.bib99)) and MORLHF (Wu et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib87); Bai et al., [2022](https://arxiv.org/html/2502.14860v2#bib.bib4)). Additionally various works have highlighted efficient methods to integrate multiple objectives, for example, through combining reward model or adapter weights (Rame et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib57); Chronopoulou et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib10); Ramé et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib58)).
+
+#### Synthetic Data.
+
+With the growing capabilities of LLMs and to supplement human data, which can be sparse, synthetic data generation with LLMs have become a popular method (Long et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib41); Xu et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib90)), especially to perform task-specific post-training (Kim et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib33)). Synthetic data is especially appealing in healthcare domain as privacy issues can make data access prohibitive (Ramesh et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib59); [Xin et al.,](https://arxiv.org/html/2502.14860v2#bib.bib89); Murtaza et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib46)). Thus, synthetic data generation through both rule-based methods (Fansi Tchango et al., [2022](https://arxiv.org/html/2502.14860v2#bib.bib14)) and LLMs (Oh et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib48); Mishra et al., [2024b](https://arxiv.org/html/2502.14860v2#bib.bib44); Yao et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib92); Wang et al., [2024b](https://arxiv.org/html/2502.14860v2#bib.bib80)) have been explored for various medical tasks. However, data to investigate question quality, especially in medicine, remains under-explored and our data generation method addresses this gap.
+
+#### Evaluation Frameworks.
+
+To assess LLMs in various medical tasks, many different evaluation frameworks have been developed, typically consisting of static, single-turn question-answering task based on multiple choice questions (Jin et al., [2020](https://arxiv.org/html/2502.14860v2#bib.bib27); Pal et al., [2022](https://arxiv.org/html/2502.14860v2#bib.bib52); Jin et al., [2019](https://arxiv.org/html/2502.14860v2#bib.bib29); Rawat et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib61)). However, with the advancement of LLMs, there has been a growing need to evaluate LLM agents beyond simple demonstration of knowledge (Thirunavukarasu et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib75)). MediQ (Li et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib38)) proposes to evaluate LLMs’ information-seeking ability through interactive clinical reasoning tasks, and constructs a benchmark based on MedQA (Jin et al., [2020](https://arxiv.org/html/2502.14860v2#bib.bib27)) leveraging information asymmetry at benchmark construction time and at inference time. MEDIC (Kanithi et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib31)) explores comprehensive assessment of LLMs using methods such as LLM-as-a-judge (Zheng et al., [2023](https://arxiv.org/html/2502.14860v2#bib.bib97)). Concurrent work HealthQ (Wang et al., [2024d](https://arxiv.org/html/2502.14860v2#bib.bib83)) analyzes attribute related factors in their evaluation, but lacks theory-grounding and does not propose methods to specifically improve the attributes. Our work builds on prior works to comprehensively assess LLM’s ability to seek information towards effective clinical communication utilizing LLM-as-a-judge and adopting MediQ framework with a newly generated set of task-specific multiple choice questions (Yao et al., [2025](https://arxiv.org/html/2502.14860v2#bib.bib93)).
+
+Appendix B Dataset Curation
+---------------------------
+
+#### HealthQ Dataset.
+
+To study clinical conversations, we utilize data from publicly available online health forum r/AskDocs 6 6 6 2013-2021 data accessed at, a subreddit consisting of both lay-users and expert users 7 7 7 Verified by moderators with photo of self with credential documents. See [https://www.reddit.com/r/AskDocs/](https://www.reddit.com/r/AskDocs/).. We parsed each subsequent comments as a single thread and consider such threads as conversations between users. Since we are interested in clinical followup questions, we first selected threads where the first followup comment from the community contained sentences ending with question marks and further decomposed each conversation into atomic questions, conclusions, and presence of positive feedback from the post author (e.g., thank you) using GPT-4o 8 8 8 gpt-4o-2024-08-06. The resulting dataset contained 17,425 threads, 13,496 unique posts, and 24,263 questions.
+
+#### Synthetic Data.
+
+We sampled questions from the above conversations to build a seed set for synthetic data generation. To ensure balanced quality for both corruption and enhancement in contrastive learning, we used proxy measures such as the expert verification status of the question author, the outcome of the conversation (e.g., final conclusions), and positive feedback from post authors. This resulted in 8 proxy quality groups. We evenly sampled from these groups, with the test set including only questions from threads with final conclusions. We created distinct train, validation, and test sets containing 4,463; 433; and 620 questions, respectively, ensuring no post overlap.
+
+#### Data Quality.
+
+We employed three key strategies to ensure data quality: (1) r/AskDocs has strict anti-misinformation policies and expert verification processes, (2) previous studies have validated the medical quality of responses in this subreddit, and (3) we filtered for samples with positive feedback and clear conclusions to ensure conversation quality.
+
+Appendix C Implementation Details
+---------------------------------
+
+### C.1 Counterfactual Perturbations
+
+To generate counterfactual perturbations, we use meta-llama/Llama-3.1-405B-Instruct-FP8 with VLLM on 8 A100 80GB GPUs with a temperature of 1.0 and max generation length of 512. See Appendix[H.1](https://arxiv.org/html/2502.14860v2#A8.SS1 "H.1 Counterfactual Perturbations ‣ Appendix H Prompts ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning") for an example prompt.
+
+#### Counterfactual Verification & Filtering
+
+To verify the quality of generated counterfactual perturbations, we use LLM-judge to rank the generated questions in the perturbed attribute (e.g., accuracy). Furthermore, we used LLM-judge result to filter generated data in §[6.4](https://arxiv.org/html/2502.14860v2#S6.SS4.SSS0.Px2 "II. Gains from Synthetic Data Quality. ‣ 6.4 Ablation Studies ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning"). To avoid self-preference bias, we used GPT-4o 9 9 9 gpt-4o-2024-08-06. See Appendix[H.2](https://arxiv.org/html/2502.14860v2#A8.SS2 "H.2 Automatic Synthetic Data Verification ‣ Appendix H Prompts ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning") for an example prompt.
+
+In Table[8](https://arxiv.org/html/2502.14860v2#A3.T8 "Table 8 ‣ Counterfactual Verification & Filtering ‣ C.1 Counterfactual Perturbations ‣ Appendix C Implementation Details ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning"), we report the percentage of pairs kept after the filter in the Enhanced-Corrupted (C-O), Enhanced-Original (E-O), and Original-Corrupted (O-C) directions, as well as the number of training and dev samples after filtering. Intuitively, since the distance between enhanced and corrupted is the largest, the LLM-judge is the most likely to accurately detect the intended perturbation direction, whereas for the Enhanced-Original and Original-Corrupted pairs, more samples fail the LLM-judge filter. We can also see that among all the attributes, clarity has the lowest data quality before filtering.
+
+Table 8: Policy Fusion DPO models on attribute groups.
+
+### C.2 Training Hyperparameters
+
+We use Open-RLHF (Hu et al., [2024a](https://arxiv.org/html/2502.14860v2#bib.bib23)) to train all models, and adopt the default hyperparameters. All models were trained on one A100 GPU and we list the hyperparameters of the final models below. Additionally, we experiment with different hyperparameter values (as listed in parentheses below) and find minimal differences in evaluation results.
+
+Supervised fine-tuning:
+
+*   •Epoch: 2 
+*   •Learning Rate: 5e-6 (1e-6, 1e-5, 5e-5) 
+*   •Warm up ratio: 0.03 
+*   •LR Schedule: `cosine_with_min_lr` 
+*   •Batch size: 256 
+
+DPO:
+
+*   •Epoch: 1 
+*   •Learning Rate: 5e-7 (1e-6) 
+*   •Beta: 2 (0.1, 1, 4) 
+*   •Warm up ratio: 0.03 
+*   •LR Schedule: `cosine_with_min_lr` 
+*   •Batch size: 256 
+
+Reward modeling:
+
+*   •Epoch: 1 
+*   •Learning Rate: 9e-6 (5e-7, 1e-6, 1e-5, 1e-4) 
+*   •Beta: 2 
+*   •Warm up ratio: 0.03 
+*   •LR Schedule: `cosine_with_min_lr` 
+*   •Batch size: 256 (4, 16, 64) 
+
+PPO:10 10 10 Note that while it’s possible to use model parallelism to train 8B PPO models with qlora, we did not have the compute resources to train 8B PPO models with the same hyperparameters and settings as the 3B counterpart, so the experiments did not include comparisons with the 8B PPO models.
+
+*   •Epoch: 1 
+*   •Learning Rate: 5e-7 
+*   •Warm up ratio: 0.03 
+*   •LR Schedule: `cosine_with_min_lr` 
+*   •Batch size: 256 
+
+Training times on single A100 GPU with batch size 256:
+
+*   •SFT: 4hr (3B model), 8.5hr (8B model) 
+*   •DPO: 10hr (3B model), 42hr (8B model) 
+*   •PPO: 48hr (3B model) 
+
+### C.3 MediQ Interactive Benchmark
+
+We evaluate the downstream performance of the trained model by generating questions in a multi-turn clinical reasoning task using MediQ (Li et al., [2024](https://arxiv.org/html/2502.14860v2#bib.bib38)). In MediQ, there is a patient agent and an expert agent interacting with each other, where the expert agent is provided some initial information in the beginning, and is expected to decide whether it wants to continue the interaction to acquire more information or terminate the interaction and provide a final answer. In this framework, the expert agent consists of three modules: abstention, question generation, and decision making. We fix the abstention and decision making modules using meta-llama/Llama-3.1-8B-Instruct, and replace the question generator with the model variants in our experiments. The goal of this evaluation is to show the effect of question quality on the final diagnostic accuracy. For reproducibility, we list the hyperparameters used in the MediQ interactive framework below:
+
+*   •Abstention strategy: Scale 
+*   •Rationale generation: True 
+*   •Self-consistency: False 
+*   •Maximum interaction length: 15 
+*   •Temperature: 0.6 
+
+Appendix D Expert vs. Non-Expert Human-Written Questions
+--------------------------------------------------------
+
+In r/AskDocs, members can upload credentials to acquire an expert flair (tag). In order to validate the quality of the LLM-judge, we aim to observe differences in the reported win-rates concerning questions generated by experts vs. non-experts. Since the original data is extremely scarce, there is limited samples where an expert and a non-expert respond to the same patient information, we design the following comparison scheme:
+
+1.   1.Starting with two sets of contexts, one with expert-written responses, one with non-expert written responses. 
+2.   2.Using a variety of models—DPO-Coarse, Alfa-DPO-DataMix, Alfa-DPO-PolicyFusion, Alfa-PPO-DataMix, Alfa-PPO-PolicyFusion—to generate responses conditioned on each set of contexts. 
+3.   3.Use our LLM-judge to compare the human written response to the model generated responses for each set of contexts. 
+4.   4.Compute the win-rates of expert vs. model and non-expert vs. model. 
+
+Following the above procedure, we find that the win-rate of non-expert responses is 35.87%, while the win-rate of expert responses is 50.52%, suggesting that expert-written questions are of higher quality than the non-expert questions. Thus, this finding validates the relative accuracy of our LLM-judge. Additionally, this also shows that the responses generated by Alfa-aligned models are approaching human-expert quality.
+
+Appendix E _MediQ-AskDocs_ Task Construction
+--------------------------------------------
+
+#### Task Construction.
+
+To construct the _MediQ-AskDocs_ task for clinical reasoning and use it in the MediQ interactive doctor-patient simulator, we need to parse each patient’s information, collected from a Reddit conversation thread, into the following components: initial information, additional information, inquiry, options, correct answer.
+
+Taking the entire conversation thread between the patient and the community member, including the initial post from the patient, as the patient’s record, we prompt o1 to extract the initial information of the patient with few shot examples. Then, in a separate call, we prompt o1 to extract the patient’s inquiry—posts in r/AskDocs are often in the form that the patient posts a paragraph of their information and asking a health question—and the conclusion from the responder if any. Finally, we treat the parsed conclusion as the correct option, and prompt the model to generate three alternative wrong answers to the inquiry.
+
+We generate multiple choice questions following the above approach for all 302 threads in the test split of the _MediQ-AskDocs_ dataset to form the novel interactive healthcare QA task.
+
+### E.1 Expert Annotations
+
+#### Task Setup.
+
+We collected human expert annotations to validate the machine-generated multiple choice questions. To validate machine-generated questions, options, and the correct answer, the task included three questions: 1) plausibility of the generated question (“Yes” or “No”), 2) selecting a correct option out of four candidates with an additional option to select “None of the above”, and 3) adding a free-text option if a plausible option is not listed. We randomly assigned maximum of 3 annotators per sample and paid 20 USD/hr. Participant recruitment process is detailed in Appendix[F](https://arxiv.org/html/2502.14860v2#A6 "Appendix F Expert Manual Evaluation ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning").
+
+#### Annotation Details.
+
+Due to recruitment constraints, we collected three expert annotation per sample over 295 samples, excluding samples annotated during recruitment.
+
+Table 9: Inter-rater agreement on multiple-choice question correct answers.
+
+#### Results.
+
+On 298 samples, excluding 4 test questions, all questions were considered plausible and correct answers generated by o1 showed 85.9% accuracy based on majority vote on 298 samples. Upon examining agreement on 295 samples, excluding 7 samples used during recruitment, we see moderate to substantial agreement (Wongpakaran et al., [2013](https://arxiv.org/html/2502.14860v2#bib.bib86); Wong et al., [2021](https://arxiv.org/html/2502.14860v2#bib.bib85)) as shown in Table[9](https://arxiv.org/html/2502.14860v2#A5.T9 "Table 9 ‣ Annotation Details. ‣ E.1 Expert Annotations ‣ Appendix E MediQ-AskDocs Task Construction ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning").
+
+Appendix F Expert Manual Evaluation
+-----------------------------------
+
+Expert manual evaluation was conducted by a panel of three research team members with extensive medical training and qualifications: two MD-PhD specialists with expertise in radiology, pathology, gastroenterology, and oncology, and one MD resident specializing in pathology. The members were instructed to rank the proposed questions given context (patient post and previous conversation—questions and answers between medical expert and patient—if any) in an online medical consultation setting similar to r/AskDocs.
+
+*PO POI Win-rate
+DPO Data 52
+Policy 41
+PPO Data 75
+Reward 74
+Policy 40
+Human 47
+
+Table 10: Win-rate of each model variation over baseline model for different attribute integration strategies based on majority vote.
+
+*PO POI AC1 CI Lower CI Upper p-value PA PE
+DPO Data 0.129-0.090 0.349 0.245 0.520 0.449
+Policy 0.240 0.047 0.434 0.016*0.620 0.500
+PPO Data 0.721 0.594 0.848<0.001***0.780 0.211
+Reward 0.680 0.543 0.817<0.001***0.750 0.219
+Policy 0.224 0.028 0.419 0.025*0.610 0.498
+*p<0.05, **p<0.01, ***p<0.001
+
+Table 11: Gwet’s AC1 inter-rater reliability results between Human and LLM Judge on win-rate over baseline by model variation. PA denotes percent agreement and PE denotes expected percent agreement by chance.
+
+### F.1 Ranking Annotation Task Setup
+
+We randomly selected 100 samples and asked our panel to rank 7 different questions per sample from best to worst follow-up questions. The seven question variations included one human-written question and six model generated questions under different experimental setup (Base Model, Alfa DPO Data, Alfa DPO Policy, Alfa PPO Data, Alfa PPO Policy, Alfa PPO Reward). The questions were presented in a randomly shuffled order, and annotation platform was constructed to not allow ties.
+
+![Image 9: [Uncaptioned image]](https://arxiv.org/html/2502.14860v2/x7.png)
+
+Figure 7: Expert preference ranking results showing pairwise win-rates. Models on y-axis are compared to the models on x-axis (e.g., Alfa PPO Data has 75% win-rate over Base Model).
+
+![Image 10: [Uncaptioned image]](https://arxiv.org/html/2502.14860v2/x8.png)
+
+Figure 8: An example of majority annotated best question (PPO Reward) and worst question (DPO Policy).
+
+### F.2 Results
+
+Similar to the results discussed in §[6.4](https://arxiv.org/html/2502.14860v2#S6.SS4.SSS0.Px1 "I. When to integrate the attributes? ‣ 6.4 Ablation Studies ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning"), human annotation win-rate shows preference towards Alfa PPO Data and Alfa PPO Reward, trained on fine-grained reward fusion as shown in Table[10](https://arxiv.org/html/2502.14860v2#A6.T10 "Table 10 ‣ Appendix F Expert Manual Evaluation ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning"). Interestingly, while on automatic evaluation on medical diagnostic accuracy, Alfa DPO Policy outperformed other integration strategies, we observe that it only outperforms Alfa PPO Policy. This diverging outcome could indicate various factors, including stylistic preferences (Zhang et al., [2024b](https://arxiv.org/html/2502.14860v2#bib.bib95)) that might influence annotation decisions which require further investigation. As noted in §[6.4](https://arxiv.org/html/2502.14860v2#S6.SS4.SSS0.Px1 "I. When to integrate the attributes? ‣ 6.4 Ablation Studies ‣ 6 Results & Analysis ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning"), our medical diagnostic accuracy evaluation relies on patient provided information; however, as shown in an example in Figure[8](https://arxiv.org/html/2502.14860v2#A6.F8 "Figure 8 ‣ F.1 Ranking Annotation Task Setup ‣ Appendix F Expert Manual Evaluation ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning"), the best quality question from Alfa PPO Reward is more open-ended and targets multiple aspects, which might not have been answered by the patient in our data.
+
+#### Inter-rater Agreement
+
+Medical experts had a substantial pairwise ranking agreement of .481.481 Gwet’s AC1 score. The experts also showed high agreement with LLM judge, especially for model variations with higher win-rate. Experts noted that questions with lower win-rate showed similar quality leading to random assignment of preferences as ties were not allowed on our platform, which could explain the lower agreement for model variations with lower win-rate.
+
+Table 12: Qualitative example of generated questions. The questions are ordered from best (Alfa-PPO-Data) to worst (Human) quality based on human annotation according to majority vote.
+
+Appendix G Qualitative Analysis
+-------------------------------
+
+Table[12](https://arxiv.org/html/2502.14860v2#A6.T12 "Table 12 ‣ Inter-rater Agreement ‣ F.2 Results ‣ Appendix F Expert Manual Evaluation ‣ Alfa: Aligning LLMs to Ask Good Questions A Case Study in Clinical Reasoning") shows an example of a multi-turn conversation and generated follow-up questions ordered by ranking of best to worst quality as denoted by our team of medical experts. Alfa-PPO-Data ranked best out of all options and asks a comprehensive and targeted follow-up question. Moreover, the example shows that even while prompted to ask questions, and trained to do so, asking follow-up questions is a difficult task as shown by the response by base model and Alfa-DPO-Data.
+
+Appendix H Prompts
+------------------
+
+### H.1 Counterfactual Perturbations
+
+You are a medical assistant and your task is to rewrite medical questions posted to an online health forum to vary some of their properties. The goal is to generate these diverse counterfactual questions to study the properties of clinical questions. You will be given a patient’s post, and the original clinician response, and you should rewrite the clinician response according to the instructions below.***PATIENT POST*** title post***CLINICIAN RESPONSE*** question***INSTRUCTION*** Rewrite the clinician response so that it is less clear/more ambiguous for the patient, while keeping everything else constant. The definition of this property and what it means for this property at varying scales are given below:Definition: The ease with which a reader can understand the intent and meaning of the question. A clear question avoids ambiguity and vagueness, providing enough detail to prevent misunderstanding, while avoiding excessive complexity or overloading with jargon. Very ambiguous: The question is highly ambiguous, vague, or disorganized, making it very difficult to understand what the asker is seeking. The question may lead to multiple interpretations and confusion. Somewhat ambiguous: The question is somewhat ambiguous or vague and may include overly complex phrasing. It requires significant effort to interpret. In-between: The question is mostly understandable but could benefit from rewording or simplification to remove partial ambiguity or excessive jargon. Somewhat clear: The question is generally clear, with minimal ambiguity, and can be understood by a layperson. There is little chance of misunderstanding. Very clear: The question is entirely unambiguous, easy to understand, and structured in a logical, concise manner. No jargon or unnecessary complexity.Additional Tips for Clear Questions Use specific time frames: Instead of “lately,” try “in the past week” or “since your last visit.” Break down complex questions: If a question could be answered in multiple ways, consider asking two separate questions. Avoid medical jargon: Use plain language that patients without a medical background can understand.Please make the rewritten question more realistic -- something that clinicians would ask in an actual patient interaction.Return the rewritten question ONLY and do not include any other text. ***REWRITTEN RESPONSE***
+
+### H.2 Automatic Synthetic Data Verification
+
+SYSTEM: Please act as an impartial judge and evaluate the quality of the responses provided by three medically trained AI assistants in a medical interaction. Carefully read the questions being asked by these expert systems as a response to the medical interaction and rank them in the provided dimensions. Begin your evaluation by comparing the three responses and provide a short explanation. Avoid any position biases and ensure that the order in which the responses were presented does not influence your decision. Do not allow the length of the responses to influence your evaluation. Ignore possible spelling or grammar mistakes and focus only on the content of the text. Be as objective as possible. The only ranking choice is ">" (greater than). For each dimension listed, provide your answer in the following example JSON format: { ‘‘dimension_name’’: { ‘‘ranking’’: ‘‘A B C’’, ‘‘reasoning’’: ‘‘Provide a clear and concise explanation for your ranking decision here.’’ } } 
+
+USER: Please carefully review the previous interaction below, which includes patient post, title, and subsequent responses if any. ***PREVIOUS MEDICAL INTERACTION*** prev_context ***MEDICAL AI QUESTIONS TO PATIENT*** - **Question A:** question_a - **Question B:** question_b - **Question C:** question_c ***EVALUATION DIMENSIONS*** dimensions ***SUPPLEMENTARY INFORMATION*** To help you evaluate the questions, please refer to the provided additional information regarding the final conclusion of this patient’s case below: Final diagnosis: final_diagnosis Conclusion: conclusion
+
+### H.3 _MediQ-AskDocs_ Task Construction
+
+SYSTEM: You are a experienced expert working in the field of medicine education. Based on your understanding of basic and clinical science, medical knowledge, and mechanisms underlying health, disease, patient care, and modes of therapy, you are given a patient case and you are tasked to parse the patient’s inquiry into a multiple choice question. The generated multiple choice should consist of a question and 4 options, which could be answered by the given patient conversation. Base your response on the current and standard practices referenced in medical guidelines. The created question should be answerable only with the patient information, rather than testing some hardcore scientific foundational knowledge recall. The questions should be faithful to the original patient’s inqiury in their post. The correct answer should be correct, and the distractors should be plausible. The correct answer should be evenly distributed among the available options to enhance the quality and reliability of the questions. The output should be in json format. 
+
+USER: You could use some parsed auxiliary information such as the final diagnosis and conclusion. Make sure that the multiple choice question you generate is not too easy but also not impossible to answer. Based on this patient record, faithfully generate a multiple choice questions according to the patient inquiry and store them in the following json format: 
+
+{ 
+
+"question": [generated question 1], 
+
+"optionA": [option A], 
+
+"optionB": [option B], 
+
+"optionC": [option C], 
+
+"optionD": [option D], 
+
+"correct_answer": [A or B or C or D] 
+
+} 
+
+After you generate the question, do a round of revision. In your revision, you should: 
+
+1. Identify any medical inaccuracies in your first response, corrsect them if any exists. 
+
+2. Make sure the question is what the patient is asking for or concerned about in their post. 
+
+3. The correct answer is indeed correct, if none of the options are correct or more than one options are correct, revise the options to improve the question. 
+
+4. Ensure that the correct answer is in a random position among the available options (shuffle if necessary) to enhance the quality and reliability of the questions. 
+
+5. Guarantee that the json output is parsable. 
+
+Respond with the final revised question in the json format and NOTHING ELSE.