Papers by Gaeul Kwon

2 papers
Tutor-ICL: Guiding Large Language Models for Improved In-Context Learning Performance (2024.findings-emnlp)

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Challenge: In-context learning (ICL) is a dominant paradigm in natural language processing.
Approach: They propose a prompting method for classification tasks using exemplar answers in a *comparative format' they also propose introducing a test instance before the exemplars to improve performance .
Outcome: The proposed method achieves up to 13.76% increase in accuracy on classification tasks across decoder-only and encoder-decoder LLMs.
On the Versatility of Sparse Autoencoders for In-Context Learning (2025.findings-emnlp)

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Challenge: Sparse autoencoders (SAEs) are emerging as a key analytical tool in interpretability for large language models.
Approach: They propose to use SAEs to extract knowledge from billions of tokens for sparse reconstruction.
Outcome: The proposed model can extract knowledge from billions of tokens for sparse reconstruction.

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