Language models deploy multidimensional internal confidence representations and threshold-based policies to control abstention behavior, with causal support from activation steering experiments.
Characterizing llm abstention behavior in science qa with context perturbations.arXiv preprint arXiv:2404.12452
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ERA models internal and external knowledge as independent Dirichlet belief masses and uses Dempster-Shafer Theory to quantify conflicts, enabling better abstention decisions in RAG systems.
Introduces a Bayesian framework viewing LLM prompts as textual parameters and proposes MHLP, a novel MCMC algorithm using LLM proposals, to perform inference and improve accuracy plus uncertainty quantification on benchmarks.
citing papers explorer
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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.
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ERA: Evidence-based Reliability Alignment for Honest Retrieval-Augmented Generation
ERA models internal and external knowledge as independent Dirichlet belief masses and uses Dempster-Shafer Theory to quantify conflicts, enabling better abstention decisions in RAG systems.
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Textual Bayes: Quantifying Prompt Uncertainty in LLM-Based Systems
Introduces a Bayesian framework viewing LLM prompts as textual parameters and proposes MHLP, a novel MCMC algorithm using LLM proposals, to perform inference and improve accuracy plus uncertainty quantification on benchmarks.