To Defer or Not to Defer? Online Learning with LLMs and Congested Experts

Key points

• Modern AI systems increasingly combine large language models (LLMs) with human experts, using the LLM to handle routine queries while deferring uncertain cases for expert review.1
• When expert capacity is limited, however, deferral decisions must account not only for the reliability of the LLM but also for congestion in the expert queue.1
• We formulate this as an online sequential decision-making problem in which, for each query, the system observes the confidence signal of the LLM's answer and must decide whether to output the answer or defer the query to a capacity-constrained expert.1
• For $K$ confidence levels and horizon $T$, KL-PBD achieves $O(K\log T)$ gap-dependent regret when the accuracy at every confidence level is bounded away from the deferral boundary, and $\widetilde{O}(\sqrt{KT})$ worst-case regret.1

Sources (1)

• [1]To Defer or Not to Defer? Online Learning with LLMs and Congested Experts
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 10, 07:14 PM

  We propose KL-PBD, a learning algorithm that uses Kullback--Leibler lower confidence bounds on the unknown reliability parameters to guide deferral decisions.

  Modern AI systems increasingly combine large language models (LLMs) with human experts, using the LLM to handle routine queries while deferring uncertain cases for expert review.

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