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When a language model is asked whether its own answer is correct, three languages are involved: the question language, the response language, and the verifier language.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.32144
#MachineLearning
An external model trained on a policy's behavioral traces can estimate confidence as accurately as the policy itself. For LLMs trained with value-based RL, the existing critic can be reused as an off-the-shelf confidence estimator.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.31820
#MachineLearning
The Schema Coherence Inventory (SCI) is a 96-item forced-choice test that scores language model outputs by length-normalized log-likelihood without generating or parsing text.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.31683
#MachineLearning
Large language models can achieve distributional alignment and generalize to unseen questions of the same domain, but generalization can be unpredictable and unstable.
Source: arXiv cs.CL
https://arxiv.org/abs/2610.31687
#MachineLearning
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