Papers by Eunji Kim
Semantic Token Reweighting for Interpretable and Controllable Text Embeddings in CLIP (2024.findings-emnlp)
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| Challenge: | Despite the varying significance of textual elements within a sentence depending on the context, efforts to account for variation of importance in constructing text embeddings have been lacking. |
| Approach: | They propose a framework for Semantic Token Reweighting to build Interpretable text embeddings which incorporates controllability as well. |
| Outcome: | The proposed framework improves the text encoding process in CLIP by differentially weighting semantic elements based on contextual importance, enabling finer control over emphasis responsive to data-driven insights and user preferences. |
Interpretation of NLP models through input marginalization (2020.emnlp-main)
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| Challenge: | Existing methods to interpret NLP predictions replace each token with a predefined value, resulting in misleading interpretations. |
| Approach: | They propose to marginalize each token out of the training data distribution to demystify the "black box" property of deep neural networks for natural language processing. |
| Outcome: | The proposed method marginalizes each token out of the training data distribution. |