Papers by Jihun Lee
Interactive Text-to-Image Retrieval with Large Language Models: A Plug-and-Play Approach (2024.acl-long)
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| Challenge: | primarily addressed in text-to-image retrieval task using dialogue-form context query . conventionally, text-based retrieval methods rely on initial text descriptions . |
| Approach: | They propose a plug-based retrieval method that uses large language models as questioners to generate non-redundant questions about the attributes of the target image. |
| Outcome: | The proposed method performs better than zero-shot and fine-tuned baselines in benchmarks. |
Piece of Table: A Divide-and-Conquer Approach for Selecting Subtables in Table Question Answering (2026.acl-long)
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| Challenge: | Existing approaches to QA tables rely on independent row or column selection, fail to capture cross-row and cross-column dependencies, or attempt global reasoning. |
| Approach: | They propose a divide-and-conquer subtable selection framework that aggregates local evidence without requiring explicit global reasoning. |
| Outcome: | The proposed framework outperforms previous approaches to table QA in the noisy context. |
A Cross-Sentence Latent Variable Model for Semi-Supervised Text Sequence Matching (P19-1)
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| Challenge: | Existing approaches to text matching consider each sequence separately . a proposed model uses both sequences to generate a given relationship with a source sequence . |
| Approach: | They propose a latent variable model for predicting the relationship between a pair of text sequences by generating a sequence that has a given relationship with a source sequence. |
| Outcome: | The proposed model achieves state-of-the-art on natural language inference and paraphrase identification. |