Papers by Minsu Kim
Improving Multi-lingual Alignment Through Soft Contrastive Learning (2024.naacl-srw)
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| Challenge: | Existing methods to train multi-lingual sentence embeddings ruins the mono-lingual space. |
| Approach: | They propose a method to align multi-lingual embeddings based on similarity of sentences measured by a pre-trained mono-lingual teacher model. |
| Outcome: | The proposed method outperforms existing multi-lingual embeddings including LaBSE on five languages and on a translation pair for Tatoeba dataset. |
Where Visual Speech Meets Language: VSP-LLM Framework for Efficient and Context-Aware Visual Speech Processing (2024.findings-emnlp)
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| Challenge: | Visual speech processing requires context modeling due to the ambiguous nature of lip movements. |
| Approach: | They propose a framework to maximize the context modeling capability by bringing the power of LLMs. |
| Outcome: | The proposed framework maximizes the power of visual speech processing by bringing it to the forefront of the field. |
Epistemology of Language Models: Do Language Models Have Holistic Knowledge? (2024.findings-acl)
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| Challenge: | et al., 2021) explores whether language models exhibit characteristics consistent with epistemological holism . authors examined the epistle of language models from the perspective of abduction, revision, and argument generation tasks. |
| Approach: | They examine whether language models exhibit characteristics consistent with epistemological holism . they created a scientific reasoning dataset and examined the epistology of language models . |
| Outcome: | The language models showed that they did not distinguish between core and peripheral knowledge, compared with other tasks. |
From Evidence to Belief: A Bayesian Epistemology Approach to Language Models (2025.naacl-long)
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| Challenge: | Existing studies on language models have focused on factual correctness and justification, but prior research has focused on the factual truth condition and justifier. |
| Approach: | They analyze language models’ responses and confidence using verbalized confidence, token probability, and sampling to examine their knowledge of Bayesian epistemology. |
| Outcome: | The language models that follow the Bayesian confirmation assumption with true evidence show varying performance depending on the degree of irrelevance, indicating they deviate from Bayes' assumptions. |