Research Paper#Large Language Models (LLMs), Conversational AI, Behavior Elicitation, Evaluation🔬 ResearchAnalyzed: Jan 3, 2026 17:00
Eliciting Behaviors in Multi-Turn Conversations
Published:Dec 29, 2025 18:57
•1 min read
•ArXiv
Analysis
This paper addresses the critical problem of evaluating large language models (LLMs) in multi-turn conversational settings. It extends existing behavior elicitation techniques, which are primarily designed for single-turn scenarios, to the more complex multi-turn context. The paper's contribution lies in its analytical framework for categorizing elicitation methods, the introduction of a generalized multi-turn formulation for online methods, and the empirical evaluation of these methods on generating multi-turn test cases. The findings highlight the effectiveness of online methods in discovering behavior-eliciting inputs, especially compared to static methods, and emphasize the need for dynamic benchmarks in LLM evaluation.
Key Takeaways
- •Extends behavior elicitation techniques to multi-turn conversations.
- •Introduces a generalized multi-turn formulation for online elicitation methods.
- •Demonstrates the effectiveness of online methods in discovering behavior-eliciting inputs.
- •Highlights the need for dynamic benchmarks in LLM evaluation.
Reference
“Online methods can achieve an average success rate of 45/19/77% with just a few thousand queries over three tasks where static methods from existing multi-turn conversation benchmarks find few or even no failure cases.”