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Found 3 papers, 1 papers with code
A Careful Examination of Large Language Model Performance on Grade School Arithmetic
Large language models (LLMs) have achieved impressive success on many benchmarks for mathematical reasoning.
Out-of-Distribution Detection & Applications With Ablated Learned Temperature Energy
As deep neural networks become adopted in high-stakes domains, it is crucial to be able to identify when inference inputs are Out-of-Distribution (OOD) so that users can be alerted of likely drops in performance and calibration despite high confidence.
A Baseline Analysis of Reward Models' Ability To Accurately Analyze Foundation Models Under Distribution Shift
These reward models are additionally used at inference-time to estimate LLM responses' adherence to those desired behaviors.
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