Papers by Bumsoo Kim
FQ-Eval: Building Evaluation Dataset for User-centered Follow-up Question Generation (2025.emnlp-industry)
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| Challenge: | Existing studies focus on enhancing information-seeking or topical relevance, often missing how follow-up questions could satisfy users’ intrinsic needs and conversational goals. |
| Approach: | They propose a user-centered evaluation dataset for assessing follow-up question generation in chat-LLM services that incorporates realistic chat-llm usage scenarios and five distinct human-aligned criteria. |
| Outcome: | The proposed model captures human-aligned criteria for the evaluation of various models and services. |
See It All: Contextualized Late Aggregation for 3D Dense Captioning (2024.findings-acl)
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| Challenge: | Recent approaches to 3D dense captioning struggle with contradicting objectives . SIA generates captions with different region of interest and aggregates them afterwards . |
| Approach: | They propose a transformer pipeline that engages in 3D dense captioning with a new paradigm . SIA decodes two sets of queries—context query and instance query—and then aggregates them afterwards . |
| Outcome: | The proposed pipeline generates captions with different region of interest and aggregates them afterwards to enhance local-global sensitivity. |
Retrieval Enhanced Feedback via In-context Neural Error-book (2025.emnlp-main)
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| Challenge: | Existing methods for learning from errors lack a structured framework for analyzing and mitigating errors, especially in Multimodal Large Language Models (MLLMs). |
| Approach: | They propose a teacher-student framework that systematically structures errors to deliver targeted feedback for multimodal reasoning. |
| Outcome: | The proposed framework improves inference efficiency, token usage, and scalability by building a query-based structure that prioritizes visual information, diagnoses failure points, and guides corrective actions. |