Papers by Dongwon Kim
LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles (2025.findings-emnlp)
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Ho Yin Sam Ng, Edward Hsu, Aashish Anantha Ramakrishnan, Branislav Kveton, Nedim Lipka, Franck Dernoncourt, Dongwon Lee, Tong Yu, Sungchul Kim, Ryan A. Rossi, Ting-Hao Kenneth Huang
| Challenge: | Figure captions are crucial for helping readers understand and remember a figure’s key message. |
| Approach: | They propose a dataset for personalized figure caption generation with multimodal figure profiles that provide inputs and profiles for each figure . |
| Outcome: | The proposed dataset provides inputs and profiles for personalized figure caption generation with multimodal figure profiles. |
FastKV: Decoupling of Context Reduction and KV Cache Compression for Prefill-Decoding Acceleration (2026.findings-acl)
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| Challenge: | Large language models (LLMs) excel at handling long-context sequences, but require substantial prefill computation and key-value (KV) cache. |
| Approach: | They propose a KV cache compression framework that decouples prefill computation from decoding KV budget. |
| Outcome: | The proposed framework reduces latency in prefill and decoding by leveraging the stabilization of token importance in later layers. |
Extending CLIP’s Image-Text Alignment to Referring Image Segmentation (2024.naacl-long)
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| Challenge: | Referring Image Segmentation (RIS) is a cross-modal task that aims to segment an instance described by a natural language expression. |
| Approach: | They propose a framework that leverages the cross-modal nature of CLIP for RIS by leveraging image-text alignment knowledge in CLIP's image-embedding space. |
| Outcome: | The proposed framework outperforms CLIP-based methods on all three major RIS benchmarks and outperformed previous CLIP methods. |
ScholarBench: A Bilingual Benchmark for Abstraction, Comprehension, and Reasoning Evaluation in Academic Contexts (2025.findings-emnlp)
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| Challenge: | ScholarBench evaluates domain-specific knowledge of large language models (LLMs) prior benchmarks lack the scalability to handle complex academic tasks. |
| Approach: | ScholarBench evaluates the academic reasoning ability of large language models . the benchmark is constructed through a three-step process . |
| Outcome: | ScholarBench evaluates the academic reasoning ability of large language models . the benchmark comprises 5,031 examples in Korean and 5,309 examples in English . |
Hybrid-Vector Retrieval for Visually Rich Documents: Combining Single-Vector Efficiency and Multi-Vector Accuracy (2026.findings-acl)
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| Challenge: | Existing approaches to document retrieval are coarse and efficient, but expensive. |
| Approach: | a plug-and-play two-stage hybrid-vector framework is proposed to retrieve visually rich documents . HEAVEN efficiently retrieves candidate pages using a single-vektor method over VS-Pages . it also reranks candidates with a multi-vecctor method while filtering query tokens by linguistic importance . |
| Outcome: | HEAVEN achieves 99.87% of the Recall@1 performance of multi-vector models on average . it reduces per-query computation by 99.8%, achieving efficiency and accuracy . |