A factorized study finds raw hidden states and attention features hard to beat in-domain for LLM uncertainty probes, but structured compressed features are more robust under distribution shift, with pretrained probes transferring to open-ended generation.
Factual Confidence of LLM s: on Reliability and Robustness of Current Estimators
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CL 2years
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DORA Explorer boosts LLM agent exploration without training by ranking diverse actions using log-probabilities and a tunable parameter, yielding UCB-competitive results on multi-armed bandits and gains on text adventure environments.
citing papers explorer
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From Signals to Transfer: A Factorised Study of Probe-Based Uncertainty Estimation in Large Language Models
A factorized study finds raw hidden states and attention features hard to beat in-domain for LLM uncertainty probes, but structured compressed features are more robust under distribution shift, with pretrained probes transferring to open-ended generation.
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DORA Explorer: Improving the Exploration Ability of LLMs Without Training
DORA Explorer boosts LLM agent exploration without training by ranking diverse actions using log-probabilities and a tunable parameter, yielding UCB-competitive results on multi-armed bandits and gains on text adventure environments.