They Infer What You Meant: Models Represent Communicative Intent More Reliably Than They Act On It
arXiv:2607.03598v1 Announce Type: cross Abstract: When a person shares something with a language model, the model often answers the surface of the message rather than what the sender was doing by sending it: share a finished project and it critiques the code; share a raw late-night line and it runs a wellness check. We treat the sender's communicative intent, the Gricean what-was-meant, as a first-class interpretability object, and show the failure is one of readout on top of a robust representation. A linear probe decodes the sender's intent, whether they want a thing recognized or evaluated,
The proliferation of advanced language models has exposed a critical gap in their ability to truly understand and act upon human communicative intent, making this research timely.
Understanding and addressing the disconnect between a language model's internal representation of intent and its external behavior is crucial for developing more reliable and human-aligned AI systems.
This research suggests that models may already possess a robust internal representation of intent, shifting the challenge from fundamental understanding to effective readout and action mechanisms.
- · AI developers
- · AI users
- · NLP researchers
- · Ethical AI advocates
- · Companies relying on superficial AI interactions
- · Generative AI tools with poor user experience
- · Researchers focused solely on surface-level model outputs
- · Users frustrated by AI misunderstandings
Language models will become more adept at understanding and responding to the underlying purpose of user prompts, rather than just their literal meaning.
This improved intent understanding could lead to more sophisticated and personalized AI assistants and autonomous agents that anticipate user needs more effectively.
Enhanced AI alignment with human intent could accelerate AI integration into critical decision-making processes, potentially altering human-machine collaboration paradigms significantly.
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Read at arXiv cs.AI