Papers by Jaehyun Park

2 papers
Zero-shot Multimodal Document Retrieval via Cross-modal Question Generation (2025.emnlp-main)

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Challenge: Existing multimodal large language models struggle when faced with unseen domains or languages.
Approach: They propose a framework that leverages the broad knowledge of an MLLM to generate cross-modal pre-questions (preQs) before retrieval.
Outcome: Experiments show that PREMIR outperforms existing retrievers on out-of-distribution benchmarks, including closed-domain and multilingual settings, outperforming strong baselines across all metrics.
RAC: Retrieval-augmented Conversation Dataset for Open-domain Question Answering in Conversational Settings (2024.emnlp-industry)

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Challenge: Existing studies constrain questions and answers within predefined contexts, excluding the retrieval process.
Approach: They present a retrieval-augmented conversation dataset that addresses key challenges . they propose a system that combines query rewriting and retrieval with reranking .
Outcome: The proposed system improves query rewriting, retrieval, reranking, and response generation performance.

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