SIT-Eval: Situation-Based Interactive Turn-by-Turn Evaluation of Conversational Agents

SIT‑Eval is a framework that evaluates conversational agents by sampling diverse situations at each turn and judging responses against situational rules. It uses an LLM‑interlocutor to generate uniformly sampled contexts and a context‑aware LLM‑judge to assess rule compliance. Benchmarking eight large language models on 1,292 turns uncovered hidden failure modes, showing that models with similar overall scores can differ greatly in rule adherence, especially in ambiguity clarification and Socratic questioning.

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• llms
• agents
• benchmarks
• safety