Real-world tasks such as giving legal or technical advice often depend on context that is initially missing at the outset. The ability to derive missing factual information by asking clarifying questions (ACQ) is an important element of real-life collaboration on such reasoning tasks. Although intent disambiguation has been heavily investigated, factual reasoning remains underexplored. To enable evaluation of factual domain clarification question generation, we present a new task that focuses on the ability to elicit missing information in multi-hop reasoning tasks. We observe that humans outperform GPT-4o by a large margin, while Llama 3 8B Instruct does not even beat the dummy baseline in some metrics. Finally, we find that by fine-tuning Llama 3 8B Instruct on its own generations filtered via rejection sampling, we can improve information recovery by 27.6% without using any manually labeled data.

Many real-world eligibility problems, ranging from medical diagnosis to tax planning, can be mapped to decision problems expressed in natural language, wherein a model must make a binary choice based on the features of the user. Large-scale domains such as legal codes or frequently updated funding opportunities render human annotation (e.g., web forms or decision trees) impractical, suggesting a need for agents that can automatically assist in decision-making. Since relevant information is often only known to the user, it is important that these agents can ask the right questions. To evaluate this task, we propose BeNYfits, a new benchmark for determining user eligibility for multiple overlapping social benefits opportunities through interactive decision-making. Our experiments show that current language models struggle with frequent hallucinations, with GPT-4o scoring only 35.7 F1 using a ReAct-style chain-of-thought. We therefore introduce ProADA, a novel approach that uses program synthesis to assist in decision-making by mapping dialog planning to a code generation problem and using gaps in structured data to determine the best next action. Our agent, ProADA, improves the F1 score to 56.2 while using nearly the same number of dialog turns.