arXiv:2606.00846v1 Announce Type: new Abstract: Users increasingly face the challenge of selecting an appropriate LLM for a given task from a rapidly growing pool of LLMs, each with distinct but often opaque latent properties. Compounding this challenge, users may lack the vocabulary or awareness to explicitly articulate the characteristics they value in an LLM's responses or deployment. We propose an interaction-efficient active learning framework in which a dueling bandit algorithm iteratively selects pairs of LLMs, collects user feedback about their responses, and updates its belief about t
The rapid proliferation of diverse LLMs makes selection increasingly complex, necessitating automated solutions for optimizing user experience and deployment efficiency.
This development improves user-LLM interaction, potentially democratizing access to optimal AI tools and accelerating their adoption across various applications.
The process of LLM selection can become more efficient and personalized through automated matchmaking, reducing friction for users and developers.
- · LLM developers (with superior models)
- · AI platform providers
- · Businesses leveraging LLMs
- · End-users of LLMs
- · LLM developers (with opaque or inferior models)
- · Manual LLM evaluation services
Users find it easier to identify the best-fit LLM for their specific needs, leading to more effective AI deployments.
Increased competition among LLMs based on performance and user satisfaction, rather than just brand recognition or marketing.
The development of 'meta-LLMs' or orchestration layers that dynamically select and combine LLMs based on real-time task demands and user feedback.
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Read at arXiv cs.LG