Multimodal Prompt Optimization: Why Not Leverage Multiple Modalities for MLLMs
Yumin Choi · Dongki Kim · Jinheon Baek · Sung Ju Hwang
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Yumin Choi · Dongki Kim · Jinheon Baek · Sung Ju Hwang
Patara Trirat · Wonyong Jeong · Sung Ju Hwang
Bedionita Soro · Aoxuan Zhang · Bruno Andreis · Jaehyeong Jo · Song Chong · Sung Ju Hwang
Sangwon Jang · Taekyung Ki · Jaehyeong Jo · Jaehong Yoon · Soo Ye Kim · Zhe Lin · Sung Ju Hwang
Minju Seo · Jinheon Baek · Seongyun Lee · Sung Ju Hwang
Taekyung Ki · Sangwon Jang · Jaehyeong Jo · Jaehong Yoon · Sung Ju Hwang
Woongyeong Yeo · Kangsan Kim · Jaehong Yoon · Sung Ju Hwang
Woongyeong Yeo · Kangsan Kim · Soyeong Jeong · Jinheon Baek · Sung Ju Hwang
Retrieval-Augmented Generation (RAG) has shown substantial promise in improving factual accuracy by grounding model responses with external knowledge relevant to queries. However, most existing approaches are limited to a text-only corpus, and while recent efforts have extended RAG to other modal...
Soyeong Jeong · Taehee Jung · Sung Ju Hwang · Joo-Kyung Kim · Dongyeop Kang
Recent Long-Context Language Models (LCLMs) can process hundreds of thousands of tokens in a single prompt, enabling new opportunities for knowledge-intensive multi-hop reasoning by integrating large sets of retrieved documents or, in some cases, directly all necessary information. However, simpl...