274 citations · 649 across the 21 of their papers we have counts for
23 papers
Loud and Clear: Making Visual Evidence Count in Multimodal Document RAG
Eunchan Jo, Seounghyun Jang, Sangwoo Mo
Multimodal document retrieval-augmented generation (RAG) has focused largely on retrieval, yet its answers depend on how well a vision-language model reads the retrieved pages. We…
Why, Where, How: Taxonomy-guided Error Grounding for Code Repair in NL2SQL
Suchan Lee, Woomin Song, Hwanjo Yu +1
SQL queries that large language models write from natural language questions can execute successfully yet produce incorrect results, so execution alone does not reveal what to fix.…
Stay Fair! Ensuring Group Fairness in Diffusion Models Across Guidance Scales
Myeongsoo Kim, Eunji Kim, Minwoo Chae +1
Diffusion models steer conditional generation with a tunable guidance scale to trade off prompt alignment and diversity. However, existing debiasing techniques are optimized for a…
Rethinking Prompt Design for Inference-time Scaling in Text-to-Visual Generation
Subin Kim, Sangwoo Mo, Mamshad Nayeem Rizve +4
Achieving precise alignment between user intent and generated visuals remains a central challenge in text-to-visual generation, as a single attempt often fails to produce the desir…
Sparsified State-Space Models are Efficient Highway Networks
Woomin Song, Jihoon Tack, Sangwoo Mo +2
State-space models (SSMs) offer a promising architecture for sequence modeling, providing an alternative to Transformers by replacing expensive self-attention with linear recurrenc…
SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs
Jaehyung Kim, Jaehyun Nam, Sangwoo Mo +5
Large language models (LLMs) have made significant advancements in various natural language processing tasks, including question answering (QA) tasks. While incorporating new infor…