Papers by Haeju Park
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. |
Soft Representation Learning for Sparse Transfer (P19-1)
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| Challenge: | Using adversarial training, we can “soft-code” shared and private spaces to avoid sparse sharing. |
| Approach: | They propose to use adversarial training to “soft-code” shared and private spaces to avoid the shared space gets too sparse. |
| Outcome: | The proposed architecture avoids sparse sharing of shared and private spaces, and also deals with low-quality input. |
Shifting from Ranking to Set Selection for Retrieval Augmented Generation (2025.acl-long)
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| Challenge: | Existing approaches primarily rerank top-k passages based on individual relevance, often failing to meet the information needs of complex queries in multi-hop question answering. |
| Approach: | They propose a set-wise passage selection approach and introduce SetR which explicitly identifies the information requirements of a query through Chain-of-Thought reasoning. |
| Outcome: | The proposed approach outperforms both proprietary LLM-based rerankers and open-source baselines in terms of answer correctness and retrieval quality. |
MICRON: Multigranular Interaction for Contextualizing RepresentatiON in Non-factoid Question Answering (D19-1)
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| Challenge: | Existing approaches for non-factoid question answering can be categorized into representation and interaction focused approaches. |
| Approach: | They propose a novel approach which derives contextualized uni-gram representation from n-grams. |
| Outcome: | The proposed approach achieves state-of-the-art in two public non-factoid question answering datasets. |
Less is More: Attention Supervision with Counterfactuals for Text Classification (2020.emnlp-main)
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| Challenge: | Specifically, we explore the advantage of counterfactual reasoning, over associative reasoning . Adding human supervision to attention has been shown to improve model predictions and explanations . |
| Approach: | They propose to use machine-augmented human attention supervision to enhance model quality. |
| Outcome: | The proposed method is more effective than existing methods requiring higher annotation cost . the proposed method can be trained to generate similar attention to human supervision . |
Learning to Embed Multi-Modal Contexts for Situated Conversational Agents (2022.findings-naacl)
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Haeju Lee, Oh Joon Kwon, Yunseon Choi, Minho Park, Ran Han, Yoonhyung Kim, Jinhyeon Kim, Youngjune Lee, Haebin Shin, Kangwook Lee, Kee-Eung Kim
| Challenge: | Situated Interactive Multi-Modal Conversations 2.0 aims to create virtual shopping assistants that can accept complex multi-modal inputs. |
| Approach: | They propose a joint learning approach that integrates visual inputs and performs all four subtasks at once for efficiency. |
| Outcome: | The proposed approach won the 10th Dialog Systems Technology Challenge (DSTC10) . it incorporates visual inputs and performs all four subtasks at once for efficiency . |