Papers by Jaeyoung Lee
Query-focused Referentiability Learning for Zero-shot Retrieval (2025.naacl-long)
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| Challenge: | Existing dense representations have shown limitations in zero-shot scenarios . however, passage representations fail to align with their gold queries . |
| Approach: | They propose a query-focused concept of 'referentiable' which ensures passage representations are referenced by their gold queries. |
| Outcome: | The proposed model outperforms existing models on the BEIR benchmark. |
D3: Dynamic Docid Decoding for Multi-Intent Generative Retrieval (2026.eacl-industry)
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| Challenge: | Existing GR systems rely on offline DocID assignment and constrained decoding . offline Doc ID assignment and decoding often prevents GR from capturing query-specific intent . |
| Approach: | They propose a mechanism that adaptively refines DocIDs through query-informed identifier expansion. |
| Outcome: | The proposed mechanism improves retrieval accuracy on unseen and multi-intent documents. |
ESG-Kor: A Korean Dataset for ESG-related Information Extraction and Practical Use Cases (2024.findings-emnlp)
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| Challenge: | Pre-trained language models are exhibiting astonishing performances in various natural language processing tasks, including classification, question answering, machine translation, summarization, and conversation generation. |
| Approach: | They built a Korean dataset to automatically extract Environmental, Social, and Governance (ESG) information from Korean companies’ sustainability reports and manually labeled it according to objective rules provided by ESG evaluation agencies. |
| Outcome: | The proposed dataset extracts environmental, social, and governance information from Korean companies’ sustainability reports and labels it according to objective rules provided by ESG evaluation agencies. |
How to Train Your Fact Verifier: Knowledge Transfer with Multimodal Open Models (2024.findings-emnlp)
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Jaeyoung Lee, Ximing Lu, Jack Hessel, Faeze Brahman, Youngjae Yu, Yonatan Bisk, Yejin Choi, Saadia Gabriel
| Challenge: | a growing influx of misinformation across news and social media is hampered by outdated foundation model training data. |
| Approach: | They propose to use large language models to scale up online policing mechanisms . they evaluate foundation model performance without continual updating . |
| Outcome: | The proposed model can improve performance without continual updating . the proposed model improves on two widely used benchmarks . |
Hierarchical Graph Convolutional Network Approach for Detecting Low-Quality Documents (2024.lrec-main)
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| Challenge: | Consistency within a document is a crucial feature indicative of its quality . low-quality documents often lack internal consistency or contain content unrelated to headlines . |
| Approach: | They propose a hierarchical graph convolutional network that detects internal inconsistencies within a document and incongruences between the title and body. |
| Outcome: | The proposed model outperforms existing models on the inconsistency dataset and on the publicly available incongruent-related dataset. |
HIL: Hybrid Isotropy Learning for Zero-shot Performance in Dense retrieval (2024.naacl-long)
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| Challenge: | Recent advances in dense retrieval models have brought ColBERT to prominence in information retrieval, but it is underperforming in zero-shot tasks. |
| Approach: | They propose a Hybrid Isotropy Learning architecture that integrates isotropic and anisotropic representations to improve zero-shot retrieval performance. |
| Outcome: | The proposed model outperforms the baseline ColBERT model in BEIR benchmarks. |
Large-scale Lifelong Learning of In-context Instructions and How to Tackle It (2023.acl-long)
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| Challenge: | In-context instruction learning is a method to improve the target PLM’s instance- and task-level generalization performance as it observes more tasks. |
| Approach: | They propose to fine-tune a Pre-trained Language Model (PLM) on a set of tasks with in-context instructions and to extend this property to a scenario in which tasks are fed to the target PLM in a sequential manner. |
| Outcome: | The proposed method achieves noticeable improvements in both types of generalization, nearly reaching the upper bound performance obtained through joint training. |
Investigating Counterfactual Unfairness in LLMs towards Identities through Humor (2026.acl-long)
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Shubin Kim, Yejin Son, Junyeong Park, Keummin Ka, Seungbeen Lee, Jaeyoung Lee, Hyeju Jang, Alice Oh, Youngjae Yu
| Challenge: | Large Language Models (LLMs) absorb social and cultural biases embedded in vast web-scale corpora and are increasingly deployed in high-stakes domains such as hiring, education, and law. |
| Approach: | They propose a framework to investigate counterfactual unfairness through humor by observing how the model’s responses change when we swap who speaks and who is addressed while holding other factors constant. |
| Outcome: | The proposed framework covers humor generation refusal, speaker intention inference, and relational/societal impact prediction tasks. |
PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules (D19-3)
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| Challenge: | a recent development of spoken dialogue systems has enabled deep learning to achieve state-of-the-art performance. |
| Approach: | They propose a Python-based domain-independent, open-source toolkit for spoken dialogue systems. |
| Outcome: | The proposed toolkit extends OpenDial's Java-based architecture and provides new functions for neural dialogue state tracking and action planning. |
From Curiosity to Clarity : Exploring the Impact of Consecutive Why-Questions (2025.findings-naacl)
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| Challenge: | a recent study has demonstrated the utility of consecutive why-questions in everyday life. |
| Approach: | They used a WHY-Chain dataset to construct a model that asked a why-questions question . they also used objectives that capture the 'consecutive' characteristic of the data . |
| Outcome: | The proposed model performed better on downstream tasks that require commonsense reasoning . the model was validated by ablation studies and the validity check . |
Adaptive Retrieval for Reasoning (2026.acl-long)
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| Challenge: | Existing reasoning-based rerankers suffer from bounded recall. |
| Approach: | They propose a framework that leverages adaptive retrieval to ensure sufficient "bridge" documents are retrieved for reasoning-intensive retrieval. |
| Outcome: | The proposed method outperforms baselines on reasoning-intensive retrieval tasks by 5.6%pt. |