aiqbench analysis
This research introduces Semantic Self-Distillation (SSD), a technique that distills uncertainty signals from large language models into lightweight student architectures to enable efficient hallucination detection and reliability scoring. By estimating semantic distributions before token generation, the method addresses the high computational cost of traditional uncertainty quantification while maintaining competitive performance on standard benchmarks like TriviaQA and MMLU. The approach offers a scalable framework for integrating principled uncertainty metrics into latency-critical AI applications without sacrificing accuracy.
Semantic Self-Distillation for Language Model Uncertainty
arXiv:2602.04577v3 Announce Type: cross Abstract: Large language models present challenges for principled uncertainty quantification, in part due to their complexity and the diversity of their outputs. Semantic dispersion, or the variance in the meaning of sampled answers, has been proposed as a useful proxy for model uncertainty, but the associated computational cost prohibits its use in latency-critical applications. We show that sampled semantic distributions can be distilled into lightweight student models which estimate a prompt-conditioned density before the language model generates an answer token. The student model predicts a semantic distribution over possible answers; the entropy of this distribution provides a prompt-level uncertainty signal, and the probability density allows answer-level reliability evaluation. Across experiments on TriviaQA and MMLU, we find our student models perform competitively relative to the teacher's sampled semantic dispersion on a hallucination prediction task, whilst offering additional uncertainty primitives for out-of-domain detection and multiple-choice answer selection. We term this technique Semantic Self-Distillation (SSD), which can serve as a general framework for distilling predictive uncertainty in complex output spaces beyond language.
Abstract:Large language models present challenges for principled uncertainty quantification, in part due to their complexity and the diversity of their outputs. Semantic dispersion, or the variance in the meaning of sampled answers, has been proposed as a useful proxy for model uncertainty, but the associated computational cost prohibits its use in latency-critical applications. We show that sampled semantic distributions can be distilled into lightweight student models which estimate a prompt-conditioned density before the language model generates an answer token. The student model predicts a semantic distribution over possible answers; the entropy of this distribution provides a prompt-level uncertainty signal, and the probability density allows answer-level reliability evaluation. Across experiments on TriviaQA and MMLU, we find our student models perform competitively relative to the teacher's sampled semantic dispersion on a hallucination prediction task, whilst offering additional uncertainty primitives for out-of-domain detection and multiple-choice answer selection. We term this technique Semantic Self-Distillation (SSD), which can serve as a general framework for distilling predictive uncertainty in complex output spaces beyond language.
Computation and Language (cs.CL)
arXiv:2602.04577 [cs.CL]
(or arXiv:2602.04577v3 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2602.04577 Focus to learn more arXiv-issued DOI via DataCite
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