LLMs represent user intent better than they act on it, study finds

Researchers have discovered that large language models possess a more robust internal representation of a user's communicative intent than their outward behavior suggests. Despite models often responding to the surface-level text rather than the underlying intention, a linear probe can accurately decode this intent from the model's hidden states across various architectures. This internal representation is more nuanced than initially apparent, generalizing to pragmatically inferred intents and distinguishing between types of support. While the models represent intent reliably, their actions on it are inconsistent and model-specific, indicating a failure in readout rather than representation. AI

IMPACT This research suggests potential for improved AI alignment and more intuitive human-AI interaction by better understanding and acting upon user intent.

RANK_REASON Research paper published on arXiv detailing findings about LLM intent representation. [lever_c_demoted from research: ic=1 ai=1.0]

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