Papers by Jaeyoung Kim
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. |
Aligning Large Language Models via Fine-grained Supervision (2024.acl-short)
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| Challenge: | Pre-trained large-scale language models often generate biased or toxic text, misaligning with human intentions. |
| Approach: | They propose to use human feedback to improve LLM alignment by fine-grained token supervision . they ask annotators to edit less preferred responses to make them more favorable . |
| Outcome: | The proposed method improves LLM alignment by up to 5.1% in terms of win rate compared with the traditional model. |
Exploring the Impact of Corpus Diversity on Financial Pretrained Language Models (2023.findings-emnlp)
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| Challenge: | Existing financial PLMs are not pretrained on sufficiently diverse financial data, leading to subpar generalization performance. |
| Approach: | They propose to pretrain financial PLMs on financial corpus and train financial models on financial data. |
| Outcome: | The proposed financial language models outperform existing financial PLMs on financial tasks even for unseen corpus groups. |
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. |
Hierarchical Retrieval with Evidence Curation for Open-Domain Financial Question Answering on Standardized Documents (2025.findings-acl)
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| Challenge: | standardized documents share similar formats and table structures . this similarity forces traditional RAG methods to misidentify near-duplicate text . |
| Approach: | They propose a hierarchical retrieval framework that performs hierarchically to reduce confusion among similar texts. |
| Outcome: | The proposed framework reduces confusion among similar documents by removing irrelevant passages . it generates complementary queries to collect missing information . |
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. |
Relevance to Utility: Process-Supervised Rewrite for RAG (2026.findings-acl)
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| Challenge: | Existing bridge modules attempt to rewrite documents for better generation, but fail to capture "document utility". |
| Approach: | They propose to observe reasoning process as process supervision and scale this observation to enhance reliability in distillation. |
| Outcome: | The proposed method improves over strong bridging baselines on multiple open-domain question-answering benchmarks. |
Bag of Tricks for In-Distribution Calibration of Pretrained Transformers (2023.findings-eacl)
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| Challenge: | Recent studies show that pre-trained language models (PLMs) often predict over-confidently. |
| Approach: | They propose to use ensemble learning and data augmentation to improve confidence calibration for PLMs by combining calibration techniques with a trade-off between accuracy and classification. |
| Outcome: | The proposed calibration method improves classification accuracy and confidence in pre-trained language models by combining several calibration techniques. |
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 . |
Pseudo Outlier Exposure for Out-of-Distribution Detection using Pretrained Transformers (2023.findings-acl)
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| Challenge: | Existing methods to detect out-of-distribution (OOD) samples are overconfident for real-world language applications. |
| Approach: | They propose a method that constructs a surrogate OOD dataset by sequentially masking tokens related to ID classes. |
| Outcome: | The proposed method can train a rejection network with ID and diverse outlier samples but requires additional data collection overhead. |
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. |