Do LLMs Know What to Ask and When? Evaluating Multi-Turn Information Seeking
When a user question is underspecified, a capable model should recognize that its context is insufficient, identify the missing information, ask for it, and respond only once that information determines a unique answer. We formalize multi-turn information seeking as solving a k-underspecified constraint satisfaction problem, where k is the number of variables jointly required to determine the target and therefore measures the degree of missing information. We instantiate the formulation in MT-InfoSeek, a controlled evaluation suite of 5,251 problems and 9,006 task instances spanning mathematics, logic, biology, medicine, and general knowledge. We evaluate models along three axes: what the...
| Authors | Yepeng Huang, Jiawen Zhang, Michelle Dai et al. |
|---|---|
| Primary category | cs.AI |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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