Papers by Kyungjae Lee
Semi-supervised Training Data Generation for Multilingual Question Answering (L18-1)
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| Challenge: | Existing datasets for question answering (QA) tasks mostly support only English . however, existing resources for these tasks are labor intensive . |
| Approach: | They propose to combine Korean QA datasets with machine-translated English resources to build seed resources. |
| Outcome: | The proposed approach leads to 71.50 F1 on Korean QA (comparable to 77.3 F1) |
Plug-and-Play Adaptation for Continuously-updated QA (2022.findings-acl)
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| Challenge: | Existing tasks to assess LMs’ efficacy as KBs do not adequately consider multiple large-scale updates. |
| Approach: | They propose a task where multiple large-scale updates are made to language models and plug-in modules are used to handle the updates. |
| Outcome: | The proposed method outperforms existing methods on zsRE QA and NQ datasets and is 4x more effective in terms of updates/forgets ratio compared to a fine-tuning baseline. |
One Missing Piece for Open-Source Reasoning Models: A Dataset to Mitigate Cold-Starting Short CoT LLMs in RL (2025.acl-industry)
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Hyungjoo Chae, Dongjin Kang, Jihyuk Kim, Beong-woo Kwak, Sunghyun Park, Haeju Park, Jinyoung Yeo, Moontae Lee, Kyungjae Lee
| Challenge: | Existing large reasoning models are limited by their closed nature and high API costs and safety issues. |
| Approach: | They propose to build a long CoT dataset with existing short CoT LLMs that are not trained for inference-time scaling. |
| Outcome: | The proposed model achieves quality comparable to—or slightly below—R1 and is able to think longer and provide control over the thought budget to better manage the overthinking problem. |
Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models (2024.emnlp-main)
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Seungone Kim, Juyoung Suk, Shayne Longpre, Bill Yuchen Lin, Jamin Shin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, Minjoon Seo
| Challenge: | Existing open-source evaluation paradigms lack flexibility and performance . language model-based evaluation is cheap and scalable, but it is difficult to evaluate . |
| Approach: | They propose a language model-based evaluation paradigm that uses a scalar indicator of quality to assess LM outputs. |
| Outcome: | The proposed language model-based evaluation model is more powerful than its predecessor. |
On Complementarity Objectives for Hybrid Retrieval (2023.acl-long)
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| Challenge: | Existing approaches to hybrid retrieval focus on sparse models to capture “residual” features neglected in spars. |
| Approach: | They propose a new objective to capture a fuller notion of complementarity . they propose to improve the model's Ratio of Complementarity to improve RoC . |
| Outcome: | The proposed method outperforms state-of-the-art methods on three representative IR benchmarks with statistical significance. |
Query Generation for Multimodal Documents (2021.eacl-main)
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| Challenge: | Existing approaches to find relevance for multimodal documents with images are expensive and require a lot of runtime overhead. |
| Approach: | They propose to attach generated queries to doc-uments and index them to narrow down to candidate matches using inverted index. |
| Outcome: | The proposed model improves relevance ranking for multimodal documents with images . the proposed model can achieve the state of the art in the first stage retrieval scenarios . |
On Sample-Efficient Code Generation (2023.emnlp-industry)
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Hojae Han, Yu Jin Kim, Byoungjip Kim, Youngwon Lee, Kyungjae Lee, Kyungmin Lee, Moontae Lee, Kyunghoon Bae, Seung-won Hwang
| Challenge: | Existing approaches to code generation rely on rejection sampling to generate multiple code snippets then select the best. |
| Approach: | They propose a framework that prioritizes sampling on test problems that models can solve. |
| Outcome: | The proposed framework reduces sampling costs while maintaining comparable code generation performance. |
Assessing LLM Reasoning Steps via Principal Knowledge Grounding (2025.findings-emnlp)
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Hyeon Hwang, Yewon Cho, Chanwoong Yoon, Yein Park, Minju Song, Kyungjae Lee, Gangwoo Kim, Jaewoo Kang
| Challenge: | Step-by-step reasoning has become a standard approach for large language models to tackle complex tasks. |
| Approach: | They propose a framework that assesses the knowledge grounding of intermediate reasoning by using a large-scale repository of atomic knowledge essential for reasoning. |
| Outcome: | The evaluation suite identifies missing or misapplied knowledge elements and provides crucial insights for uncovering fundamental reasoning deficiencies in LLMs. |
Learning to Explore and Select for Coverage-Conditioned Retrieval-Augmented Generation (2025.findings-naacl)
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| Challenge: | Recent advances of large language models (LLMs) have enabled them to provide long and detailed responses by leveraging their parametric knowledge. |
| Approach: | They introduce a dataset of 10K hierarchical sets of information-seeking subqueries that define structured boundaries for outline creation and evaluation in C2 scenarios. |
| Outcome: | The proposed model generates higher-quality outlines that better fulfill diverse user needs when trained with alignment techniques like DPO. |
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models (2025.naacl-long)
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Seungone Kim, Juyoung Suk, Ji Yong Cho, Shayne Longpre, Chaeeun Kim, Dongkeun Yoon, Guijin Son, Yejin Cho, Sheikh Shafayat, Jinheon Baek, Sue Hyun Park, Hyeonbin Hwang, Jinkyung Jo, Hyowon Cho, Haebin Shin, Seongyun Lee, Hanseok Oh, Noah Lee, Namgyu Ho, Se June Joo, Miyoung Ko, Yoonjoo Lee, Hyungjoo Chae, Jamin Shin, Joel Jang, Seonghyeon Ye, Bill Yuchen Lin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, Minjoon Seo
| Challenge: | a recent study evaluated language models using abstract evaluation criteria that lack the flexibility and granularity of human assessment. |
| Approach: | They propose a benchmark to evaluate nine distinct language models' capabilities . they use instance-specific evaluation criteria to mirror human evaluation . |
| Outcome: | The proposed benchmark evaluates nine distinct capabilities of language models across 77 tasks. |
Evidentiality-aware Retrieval for Overcoming Abstractiveness in Open-Domain Question Answering (2024.findings-eacl)
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| Challenge: | Existing approaches to ODQA use a simple yet effective retriever-reader framework, but this approach is not always effective in abstractive tasks. |
| Approach: | They propose a method that leverages synthetic distractor samples to learn to discriminate evidence passages from distractors. |
| Outcome: | The proposed method is validated on multiple abstractive open-domain question answering tasks. |
Learning with Limited Data for Multilingual Reading Comprehension (D19-1)
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| Challenge: | Existing approaches to support question answering in a new language with limited training resources introduce noises to the training data due to translation or generation errors. |
| Approach: | They propose a weakly-supervised framework that quantifies noises from automatically generated labels to deemphasize or fix noisy data in training. |
| Outcome: | The proposed framework can deemphasize or fix noisy data in training on low-resource languages with varying similarity to English. |
When to Read Documents or QA History: On Unified and Selective Open-domain QA (2023.findings-acl)
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| Challenge: | Existing work aims to answer factoid questions from an open set of domains using knowledge sources. |
| Approach: | They propose to use QA-pair and document corpora to answer open-domain questions . they propose to apply natural follow-up to both models to find answers . |
| Outcome: | The proposed method is validated on natural questions and TriviaQA. |
Robustifying Multi-hop QA through Pseudo-Evidentiality Training (2021.acl-long)
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| Challenge: | Existing approaches to robustify multi-hop question answering models require expensive annotations. |
| Approach: | They propose a method to supervise answers with right reasoning chains without annotations . they compare answers confidence with and without evidence sentences to generate "pseudo-evidentiality" annotations. |
| Outcome: | The proposed model is accurate and robust in multi-hop reasoning. |
PreWoMe: Exploiting Presuppositions as Working Memory for Long Form Question Answering (2023.emnlp-main)
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| Challenge: | Existing approaches to LFQA are limited to information-seeking questions . however, users' questions in the real-world may often mislead language models (LMs) to output misinformation. |
| Approach: | They propose a unified approach capable of handling any type of information-seeking question. |
| Outcome: | The proposed approach can handle any type of information-seeking question. |