Papers by Davis Brown

2 papers
Understanding the Inner-workings of Language Models Through Representation Dissimilarity (2023.emnlp-main)

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Challenge: Dissimilarity measures measure the extent to which two model’s internal representations differ . they can identify and locate generalization properties of models that are invisible via in-distribution test set performance.
Approach: They propose to use representation dissimilarity measures to measure the extent to which two model’s internal representations differ.
Outcome: The proposed dissimilarity measures can identify and locate generalization properties of models that are invisible via in-distribution test set performance and new evaluations of how language model features vary as width and depth are increased.
Adaptively profiling models with task elicitation (2025.emnlp-main)

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Challenge: Language model evaluations fail to characterize consequential failure modes, forcing experts to inspect outputs and build new benchmarks.
Approach: They propose a method that automatically builds new evaluations to profile model behavior.
Outcome: The proposed method finds that language models fail in hundreds of tasks . it also finds that o3-mini is prone to hallucination when fabrications are repeated .

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