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

Key points

• 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.1
• We formalize an agent's desired behavior as situational rules: if situation is P, follow rules Q.1
• At each conversation turn, an LLM-interlocutor enacts a uniformly sampled situation.1
• Benchmarking 8 LLMs on 1292 conversation turns reveals failure modes hidden by aggregate metrics: models with similar overall scores vary widely in their adherence to individual rules.1

Sources (1)

• [1]SIT-Eval: Situation-Based Interactive Turn-by-Turn Evaluation of Conversational Agents
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 03:24 PM

  We introduce SIT-Eval, an adjustable framework for evaluating situation-based 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.

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