SIT-Eval: Interactive evaluation of conversational agents through situational scenarios
In brief
- SIT-Eval evaluates conversational agents using simulated situations and precise contextual rules.
- High-performing models often fail on specific criteria like ambiguity clarification.
- The framework uses an LLM judge validated by human-verified synthetic dialogues to score responses.
SIT-Eval introduces an adjustable framework to evaluate conversational agents by simulating diverse and rare situations. Unlike traditional benchmarks, it measures adherence to precise contextual rules rather than aggregate performance. Tests on 8 models reveal hidden gaps, particularly in ambiguity clarification and Socratic questioning.
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