ExPerT: Personalizing LLM Responses to Users' Domain Expertise via Query-Wise Semantic and Keystroke Behavioral Cues
arXiv:2607.01242v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used by end users, yet existing personalization methods relying on static profiles or text-only signals fail to capture query-specific expertise variation. We present ExPerT, a query-wise personalization framework that adapts LLM responses to users' query domain expertise by combining semantic and behavioral cues. ExPerT consists of two key components: (i) a semantic-behavioral expertise inference module that jointly interprets query text and keystroke dynamics via in-context LLM prompting, and (ii)
As LLMs become increasingly ubiquitous in end-user applications, the limitations of generic responses and static personalization methods are becoming more apparent, driving innovation in context-aware adaptation.
Strategic readers should care as enhanced personalization for LLMs promises to dramatically improve user experience and efficacy, especially in specialized or professional contexts, making AI tools more valuable and sticky.
LLM responses will no longer be one-size-fits-all but will dynamically adapt to a user's inferred expertise based on their query and interaction, leading to more relevant and nuanced information delivery.
- · LLM developers
- · Professional services
- · Specialized knowledge workers
- · Human-computer interaction researchers
- · Generic LLM platforms with static personalization
- · Low-quality information providers
Increased user satisfaction and adoption of LLM-powered tools across diverse domains.
New competitive advantages for LLM providers who can offer superior, context-aware personalization.
Potential for LLMs to become highly specialized co-pilots that anticipate and cater to individual users' professional needs, transforming workflows in expert fields.
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Read at arXiv cs.CL