Papers by Alan Sun

3 papers
Deciphering Stereotypes in Pre-Trained Language Models (2023.emnlp-main)

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Challenge: Current approaches for examining stereotypes in PLMs require intricate human knowledge about these stereotypes and entail careful manual curation of examples.
Approach: They propose a framework for examining stereotype-encoding behavior of PLMs using model probing and textual analyses.
Outcome: The proposed approach can debiase PLMs without compromising their language modeling capabilities or performance.
Can Pre-trained Vision and Language Models Answer Visual Information-Seeking Questions? (2023.emnlp-main)

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Challenge: Pre-trained vision and language models have demonstrated state-of-the-art capabilities over existing tasks involving images and texts.
Approach: They analyze a visual question answering dataset tailored for info-seeking questions . they show that pre-trained visual and language models can use fine-grained knowledge .
Outcome: The proposed dataset elicits models to use fine-grained knowledge learned during pre-training.
Circuit Stability Characterizes Language Model Generalization (2025.acl-long)

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Challenge: Rapid development of state-of-the-art models induce benchmark saturation, while creating more challenging datasets is labor-intensive.
Approach: They propose to introduce circuit stability as a new way to assess model performance.
Outcome: The proposed methods characterize and predict different aspects of generalization.

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