Decision theory framework enables label-free LLM evaluation via rationality axioms
A new framework uses decision theory's representation theorems to evaluate and regularize language models without human labels or external feedback. By testing whether model outputs satisfy rationality axioms through synthetic choice problems, researchers can verify behavioral consistency and apply computational penalties for violations. This approach matters because it offers a scalable, label-free alternative to traditional RLHF and evals, grounding model assessment in formal mathematical guarantees rather than subjective human judgment. The method's completeness property means passing models cannot be rejected on rationality grounds by further tests of the same data, creating a principled ceiling for what the axioms can certify.
Modelwire context
ExplainerThe paper's key novelty isn't just label-free evaluation, but the completeness property: once a model passes the rationality axiom tests, no further tests on the same data can reject it. This creates a formal ceiling for what behavioral consistency can certify, not just another filtering method.
This work sits alongside a cluster of recent papers addressing the bottleneck in LLM post-training: the cost and unreliability of preference signals. Cloud-ScPO (early August) mines preferences from hidden-state geometry; RSTG (late July) recovers learning signals from sparse rewards; this paper sidesteps preference labels entirely by grounding evaluation in decision-theoretic axioms. All three treat preference/evaluation scarcity as the binding constraint. The difference here is philosophical: rather than extracting or inferring what humans want, this approach asks whether the model's choices are internally consistent according to formal rationality principles.
If practitioners adopt this framework for model selection in production, watch whether the rationality axioms catch failure modes that RLHF-trained models currently exhibit (e.g., preference reversals, intransitivity under prompt variation). If the axioms prove too permissive or too restrictive relative to downstream task performance within 6 months, the method's practical utility collapses despite its theoretical elegance.
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MentionsLLMs · representation theorems · decision theory
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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “Revealed Rationality: Label-Free Evaluation and Regularization from Representation Theorems”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.