Explain Like I'm 5 or Whatever I Choose: Evaluating the Interactive Potential of Language Model Responses
arXiv:2606.06788v1 Announce Type: new Abstract: Evaluations of large language models (LLMs) in scientific information seeking tasks have become increasingly use-centric, such as conducting live or multi-turn evaluations with real users. These evaluations still assume a single, static chat interface, but as models are integrated into new interfaces, evaluations must shift to incorporate interface-specific criteria. We propose a new evaluation framework based on a formative study with $16$ participants that tests models' ability to generate multiple responses to one query that differ along an in
The proliferation of Large Language Models (LLMs) across various applications necessitates advanced evaluation methods beyond static chat interfaces, especially as models are integrated into diverse new interaction paradigms.
Sophisticated evaluation frameworks that account for interactive potential are critical for developing truly adaptive and user-centric LLMs, moving beyond basic question-answering towards dynamic engagement.
The focus of LLM evaluation is shifting from single-response, static interactions to multi-response, interactive capabilities, demanding more nuanced assessment methodologies that reflect real-world usage.
- · AI researchers focusing on human-computer interaction
- · Companies developing customizable AI interfaces
- · Users seeking personalized AI interactions
- · LLM developers relying solely on static evaluation metrics
- · Applications with inflexible AI interfaces
This research provides a new framework for evaluating the interactive potential of LLMs.
Improved interactive evaluation will lead to the development of LLMs capable of adapting their responses to individual user preferences and contexts.
The ability of LLMs to generate diverse, context-appropriate responses could accelerate the adoption of AI agents in complex, personalized workflows.
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