SIT-Eval: Situation-Based Interactive Turn-by-Turn Evaluation of Conversational Agents
The rapid proliferation of conversational AI agents necessitates evaluation methods that assess performance across diverse dialogue scenarios, utilizing multiple situational criteria, as appropriate behavior depends on context and encompasses various aspects. Existing dialogue benchmarks typically use static natural conversations, which underrepresent rare situations---a critical safety gap, as low-probability events become frequent failures at production scale. However, limited sample sizes force a choice: either maintain natural dialogues for realism or turn to adaptive situation sampling for comprehensive yet statistically reliable coverage. We introduce SIT-Eval, an adjustable framewo...
| Authors | Ravil Mussabayev, Saveliy Zavertyaev, Dmitriy Parpulov et al. |
|---|---|
| Primary category | cs.CL |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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