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Estimating uncertainty in Large Language Models (LLMs) is important for properly evaluating LLMs, and ensuring safety for users.
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Detecting correlation changes in multivariate time series: A comparison of four non-parametric change point detection methods
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
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Detecting change-point, trend, and seasonality in satellite time series data to track abrupt changes and nonlinear dynamics, a bayesian ensemble algorithm
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Language models are few-shot learners
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Training verifiers to solve math word problems
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Loom: interface to the multiverse, 2021
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An explanation of in-context learning as implicit bayesian inference
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What learning algorithm is in-context learning? investigations with linear models
Ekin Akyürek, Dale Schuurmans, Jacob Andreas, Tengyu Ma, and Denny Zhou · 2022
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Seungju Han, Beomsu Kim, and Buru Chang · 2022
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Language models (mostly) know what they know
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Larger language models do in-context learning differently
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How language model hallucinations can snowball
Muru Zhang, Ofir Press, William Merrill, Alisa Liu, and Noah A Smith · 2023
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Many-shot jailbreaking
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Foundational challenges in assuring alignment and safety of large language models
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In-context learning dynamics with random binary sequences
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Large language models are zero-shot reasoners
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Chain-of-thought prompting elicits reasoning in large language models
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Sparks of artificial general intelligence: Early experiments with gpt-4
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The waluigi effect, 2023
Cleo Nardo · 2023
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A survey on evaluation of large language models
Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, et al · 2024
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Nudging: Inference-time alignment via model collaboration
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Kanishk Gandhi, Denise Lee, Gabriel Grand, Muxin Liu, Winson Cheng, Archit Sharma, and Noah D Goodman · 2024
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Learning to reason with llms, 2024
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Can llms express their uncertainty? an empirical evaluation of confidence elicitation in llms
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Benchmarking llms via uncertainty quantification
Fanghua Ye, Mingming Yang, Jianhui Pang, Longyue Wang, Derek F Wong, Emine Yilmaz, Shuming Shi, and Zhaopeng Tu · 2024
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