Papers by Bum Chul Kwon

1 papers
Finspector: A Human-Centered Visual Inspection Tool for Exploring and Comparing Biases among Foundation Models (2023.acl-demo)

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Challenge: Existing pre-trained transformer-based language models have shown exceptional performance on various benchmarks, but hidden biases can be found within these models.
Approach: They propose a human-centered visual inspection tool to detect biases in different categories through log-likelihood scores generated by language models.
Outcome: The proposed tool detects biases in different categories through log-likelihood scores generated by language models.

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