Learning to Route LLMs with Confidence Tokens
read the original abstract
Large language models (LLMs) have demonstrated impressive performance on several tasks and are increasingly deployed in real-world applications. However, especially in high-stakes settings, it becomes vital to know when the output of an LLM may be unreliable. Depending on whether an answer is trustworthy, a system can then choose to route the question to another expert, or otherwise fall back on a safe default behavior. In this work, we study the extent to which LLMs can reliably indicate confidence in their answers, and how this notion of confidence can translate into downstream accuracy gains. We propose Self-Reflection with Error-based Feedback (Self-REF), a lightweight training strategy to teach LLMs to express confidence in whether their answers are correct in a reliable manner. Self-REF introduces confidence tokens into the LLM, from which a confidence score can be extracted. Compared to conventional approaches such as verbalizing confidence and examining token probabilities, we demonstrate empirically that confidence tokens show significant improvements in downstream routing and rejection learning tasks.
This paper has not been read by Pith yet.
Forward citations
Cited by 6 Pith papers
-
Before Thinking, Learn to Decide: Proactive Routing for Efficient Visual Reasoning
PRP introduces proactive routing via Draft Rating Learning and Joint Rating Learning to route queries early between draft and target models for efficient multimodal reasoning.
-
Inference Time Optimization with Confidence Dynamics
Correct reasoning traces exhibit positive confidence gain while incorrect traces show declining confidence, enabling CDG-based voting that boosts performance on AIME, HMMT and BRUMO benchmarks across multiple LLM arch...
-
Calibration-Aware Policy Optimization for Reasoning LLMs
CAPO improves LLM calibration by up to 15% while matching or exceeding GRPO accuracy through logistic AUC loss and noise masking, enabling better abstention and scaling performance.
-
RouterWise: Joint Resource Allocation and Routing for Latency-Aware Multi-Model LLM Serving
Joint resource allocation and routing for multi-model LLM serving can produce up to 87% variation in achievable output quality across setups on the same GPU cluster.
-
Causal Evidence that Language Models use Confidence to Drive Behavior
Language models deploy multidimensional internal confidence representations and threshold-based policies to control abstention behavior, with causal support from activation steering experiments.
-
A Greedy PDE Router for Blending Neural Operators and Classical Methods
An approximate greedy router for hybrid PDE solvers that mimics optimal selection without true error access and shows faster, more stable error reduction on test equations.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.