Papers by Saehyung Lee

2 papers
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.
Unleashing Multi-Hop Reasoning Potential in Large Language Models through Repetition of Misordered Context (2025.findings-naacl)

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Challenge: Multi-hop reasoning requires multi-step reasoning based on supporting documents within a given context.
Approach: They propose a method that prompts the model by repeatedly presenting the context.
Outcome: The proposed method improves the F1 score by 30%p on multi-hop QA tasks and increases accuracy by 70%p on a synthetic task.

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