Papers by Dongwoo Kim
word2word: A Collection of Bilingual Lexicons for 3,564 Language Pairs (2020.lrec-1)
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| Challenge: | Our dataset provides top-k word translations in 3,564 (directed) language pairs across 62 languages in OpenSubtitles2018. |
| Approach: | They propose a dataset and an open-source Python package for cross-lingual word translations extracted from sentence-level parallel corpora. |
| Outcome: | The proposed bilingual lexicons have high coverage and achieve competitive translation quality for several language pairs. |
Plane Geometry Problem Solving with Multi-modal Reasoning: A Survey (2026.findings-eacl)
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| Challenge: | Plane geometry problem solving has gained significant attention as a benchmark to assess the multi-modal reasoning capabilities of large vision-language models. |
| Approach: | They present a systematic review of existing work in PGPS and summarize their results. |
| Outcome: | The proposed frameworks are compared with existing frameworks and analyze them according to their architectural designs. |
ChronoBias: A Benchmark for Evaluating Temporal Group Bias in the Time-sensitive Knowledge of Large Language Models (2025.findings-emnlp)
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| Challenge: | Using a template-based semi-automated generation method, we evaluate time-conditional group bias in time-sensitive knowledge of large language models (LLMs). |
| Approach: | They propose a template-based semi-automated generation method to construct a time-conditional group bias benchmark. |
| Outcome: | The proposed method balancing quality-quantity trade-off in existing benchmark curation approaches. |
Retrieval-Augmented Generation with Estimation of Source Reliability (2025.emnlp-main)
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| Challenge: | Retrieval-Augmented Generation (RAG) is an effective approach to enhance the factual accuracy of large language models (LLMs). |
| Approach: | They propose a multi-source RAG framework that estimates the reliability of sources and prioritizes highly reliable and relevant documents. |
| Outcome: | The proposed framework outperforms baselines in scenarios with heterogeneous source reliability while scaling efficiently as the number of sources increases. |
Exploring Iterative Controllable Summarization with Large Language Models (2026.findings-eacl)
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| Challenge: | Large language models (LLMs) excel at abstractive summarization tasks, but their ability to precisely control summary attributes remains underexplored. |
| Approach: | They propose a guide-to-explain framework for controllable summarization that enables the model to identify misaligned attributes in the initial draft and guides it to self-explan errors in the previous output. |
| Outcome: | The proposed framework generates well-adjusted summaries that satisfy the desired attributes with robust effectiveness while requiring surprisingly fewer iterations than other iterative approaches. |
GeoDANO: Geometric VLM with Domain Agnostic Vision Encoder (2025.findings-emnlp)
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| Challenge: | GeoDANO is a geometric vision-language model with a domain-agnostic vision encoder . it is currently limited to recognizing geometric features in general-purpose VLMs . |
| Approach: | They propose a geometric vision-language model with a domain-agnostic vision encoder for plane geometry problems. |
| Outcome: | The proposed model outperforms vision encoders in recognizing geometric features . it outperformed specialized methods for plane geometry problems and GPT-4o on MathVerse . |
CoPL: Collaborative Preference Learning for Personalizing LLMs (2025.emnlp-main)
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| Challenge: | Existing methods for personalizing large language models struggle with flexibility and generalization. |
| Approach: | They propose a graph-based collaborative filtering framework that models user-response relationships to enhance preference estimation in sparse annotation settings. |
| Outcome: | The proposed framework outperforms existing reward models in TL;DR, UltraFeedback-P, and PersonalLLM datasets. |