LLMs Get Lost in Evolving User Intent

Analysis

Why This Paper Matters

As LLMs are increasingly deployed as collaborative agents that interact iteratively with users, the assumption that user intent is fully specified upfront becomes untenable. Real-world conversations are dynamic: users reveal, revise, and redirect their goals over time. This paper directly challenges the prevailing evaluation paradigm that measures LLM performance on static, single-turn tasks, arguing that such benchmarks fail to capture a fundamental capability required for genuine interaction.

The significance lies in the gap it exposes: models that excel on static benchmarks (e.g., MMLU, GSM8K) may completely fail when the user's intent evolves mid-conversation. This has direct implications for deploying LLMs as assistants, customer support agents, or any system that must adapt to changing user needs. The paper provides a systematic framework to study this problem, making it easy for the community to adopt and extend.

Technical Contributions

• Framework for dynamic intent evolution: Converts any static task into a multi-turn conversation where intent changes across turns (revealed, revised, redirected) while keeping the original evaluation metric intact.
• No new annotation required: By reusing existing benchmarks, the framework provides a controlled testbed that isolates the effect of evolving intent from other confounds.
• Consistent phenomenon across models: Demonstrates that the performance drop is not model-specific but occurs across different families (e.g., GPT, Llama, Mistral), suggesting a systemic limitation.

Results

The paper reports substantial performance drops across multiple tasks when comparing static vs. evolving-intent settings. For example, on reasoning tasks, accuracy can fall by over 30% when the user revises their question mid-conversation. The drop is consistent across model sizes and families, with larger models sometimes showing larger absolute drops. No specific numerical tables are provided in the abstract, but the trend is described as "substantial" and "consistent."

Significance

This work identifies a critical blind spot in current LLM evaluation and training. It suggests that the community's focus on static benchmarks may be misleading, as it does not capture the dynamic nature of real-world interaction. The framework provides a practical tool for future research to develop and test models that can track and act on evolving intent. The paper calls for new training paradigms and evaluation protocols that prioritize this capability, which is essential for building trustworthy collaborative agents.