Feb 21, 2024 · In this work, we explore the potential of deriving confidence from the distribution of multiple randomly sampled model generations, via three measures of ...
Feb 21, 2024 · Consistency-based calibration methods outperform existing post-hoc approaches and offers practical guidance on choosing suitable consistency metrics for ...
Mar 22, 2024 · Calibration in LLMs. Defining calibration for language models is challenging, especially for vari- able length response sequences.
Calibrating language models (LMs) aligns their generation confidence with the actual likelihood of answer correctness, which can inform users about LMs' ...
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Oct 7, 2023 · In this work, we conduct a systematic examination of the calibration of aligned language models throughout the entire construction process.
Jun 7, 2024 · In this study we compare three methods of uncertainty measurement: Confidence Elicitation, Token-Level Probabilities, and Sample Consistency among large ...
In this work, we conduct a systematic examination of the calibration of aligned language models throughout the entire construction process.
Missing: Sample Consistency.
May 29, 2024 · This paper explores methods for calibrating the reasoning capabilities of large language models to improve their internal consistency.
May 22, 2024 · Calibrating large language models is a complex yet vital endeavor that enhances the reliability and safety of AI applications. By using and ...
This work proposes APRICOT (auxiliary prediction of confidence targets): A method to set confidence targets and train an additional model that predicts an ...