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New algorithms optimize LLM expert routing with limited feedback

By PulseAugur Editorial · [1 sources] ·

Researchers have developed new algorithms for adaptive routing of prompts to large language model (LLM) experts in an online setting with limited feedback. Formulated as a bandit problem, the approach aims to maximize response quality by strategically selecting and observing rewards to minimize regret. Experiments demonstrate the efficiency of these strategies in learning high-quality routing across diverse LLMs, even with a constrained feedback budget. AI

IMPACT This research could lead to more efficient and cost-effective use of large language models by optimizing prompt distribution with minimal feedback.

RANK_REASON The cluster contains a research paper published on arXiv detailing new algorithms for LLM prompt routing. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New algorithms optimize LLM expert routing with limited feedback

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The cluster contains a research paper published on arXiv detailing new algorithms for LLM prompt routing. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Wang Wei, Soumyabrata Pal, Koyel Mukherjee, Franck Dernoncourt, Ryan A. Rossi, Branislav Kveton, Hoda Eldardiry ·

    Online Learning with LLM Experts from Limited Feedback

    arXiv:2609.05820v1 Announce Type: new Abstract: We study adaptive routing of prompts to large language model (LLM) experts to maximize response quality in an online setting with limited feedback. We formulate it as a bandit problem with $K$ actions that represent experts and $d$ …

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