Benchmarking LLM Utility Recovery with User Intent Clarification

Useless but Safe? Benchmarking Utility Recovery with User Intent Clarification in Multi-Turn Conversations

In a recent study published on arXiv, researchers have explored the limitations of large language models (LLMs) in understanding user intent during multi-turn conversations. The paper, titled “Useless but Safe? Benchmarking Utility Recovery with User Intent Clarification in Multi-Turn Conversations,” examines how these models respond to seemingly harmful queries that actually stem from benign intentions. The research introduces CarryOnBench, an innovative benchmark designed to assess the ability of LLMs to refine their understanding of user intent and subsequently recover utility while maintaining safety.

Understanding CarryOnBench

CarryOnBench is the first interactive benchmarking tool that simulates conversations to evaluate LLM performance across various user queries. The study involved:

• 398 initially harmful queries with benign underlying intents.
• 5,970 simulated conversations varying user follow-up sequences.
• 14 different models evaluated on intent-aligned utility and safety.

Through this extensive testing, the researchers generated 1,866 unique conversation flows comprising between 4 and 12 turns, resulting in a total of 23,880 model responses. The evaluation method utilized, known as Ben-Util, is a checklist-based metric that assesses how effectively each model response meets the user’s benign information needs through atomic items.

Key Findings

The findings reveal significant insights into LLM performance:

• At the first turn, models met only 10.5% to 37.6% of the user’s benign information needs.
• When the benign intent was made explicit from the outset, fulfillment rates increased to between 25.1% and 72.1%.
• This discrepancy indicates that LLMs often withhold information not due to a lack of knowledge, but due to misinterpretation of user intent.

Additionally, the research highlights that with benign clarifications through multi-turn conversations, 13 out of 14 models were able to approach or exceed the initial single-turn fulfillment baseline. However, the recovery cost varied significantly across different models, leading to three identified failure modes that were not apparent in single-turn evaluations:

• Utility Lock-In: The model rarely updates its responses despite receiving clarifications.
• Unsafe Recovery: The model updates its responses but at a disproportionate safety cost.
• Repetitive Recovery: The model tends to recycle prior responses instead of providing new, relevant information.

Moreover, the study discovered that conversations tended to converge to similar levels of harmfulness, regardless of the initial conservativeness exhibited by the model. This suggests that the ability of LLMs to handle clarified user intent is significantly limited, exposing a critical gap in current single-turn evaluation methods.

Implications for Future Research

The research underscores the need for enhanced evaluation frameworks that focus not only on safety but also on the utility recovery of LLMs during multi-turn interactions. As LLMs become increasingly integrated into user-facing applications, understanding their limitations in handling user intent is essential for developing more effective and responsive AI systems.

This pioneering work paves the way for future studies to explore mechanisms that can improve the responsiveness of LLMs, ensuring they can provide valuable information while maintaining safety protocols. The conversation around LLM safety and utility recovery continues to evolve, highlighting the importance of ongoing research in the field of AI ethics and user interaction.

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