Papers by Donghyeon Jeon
SLM as Guardian: Pioneering AI Safety with Small Language Model (2024.emnlp-industry)
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Ohjoon Kwon, Donghyeon Jeon, Nayoung Choi, Gyu-Hwung Cho, Hwiyeol Jo, Changbong Kim, Hyunwoo Lee, Inho Kang, Sun Kim, Taiwoo Park
| Challenge: | Prior safety research on large language models focused on aligning them to safety requirements, but internalizing such safeguard features into larger models brought challenges of higher training cost and unintended degradation of helpfulness. |
| Approach: | They propose a multi-task learning mechanism that integrates harmful query detection and safeguard response into a single model. |
| Outcome: | The proposed approach outperforms the publicly available LLMs in harmful query detection and safeguard response generation. |
PURE: Post-hoc Unlocking and REfinement for Discrete Diffusion Decoding (2026.findings-acl)
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| Challenge: | Masked diffusion language models (MDLMs) are limited by a monotonic unmasking policy, where committed tokens cannot be revised. |
| Approach: | They propose a training-free inference algorithm for two-phase decoding that unlocks unstable regions through deterministic window masking and stochastic leftward relaxation. |
| Outcome: | The proposed algorithm significantly improves accuracy on reasoning benchmarks on GSM8K. |
Taxonomy and Analysis of Sensitive User Queries in Generative AI Search System (2025.findings-naacl)
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Hwiyeol Jo, Taiwoo Park, Hyunwoo Lee, Nayoung Choi, Changbong Kim, Ohjoon Kwon, Donghyeon Jeon, Eui Hyeon Lee, Kyoungho Shin, Lim Sun Suk, Kyungmi Kim, Lee Jihye, Sun Kim
| Challenge: | generative LLMs have been used by industries for various purposes, but limited resources and limited experience hinder their deployment and maintenance. |
| Approach: | They propose a taxonomy for sensitive search queries and outline approaches to generating generative LLMs. |
| Outcome: | The proposed model can be used to analyze sensitive queries from real users. |
LM-BFF-MS: Improving Few-Shot Fine-tuning of Language Models based on Multiple Soft Demonstration Memory (2022.acl-short)
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| Challenge: | LM-BFF (CITATION) achieves significant few-shot performance by using auto-generated prompts and adding demonstrations similar to an input example. |
| Approach: | They propose to use auto-generated prompts and add demonstrations to LM-BFF to improve few-shot fine-tuning of language models with multiple soft demonstrations. |
| Outcome: | The proposed method improves few-shot fine-tuning on eight NLP tasks. |
MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data (2023.findings-eacl)
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| Challenge: | Experimental results show that Transformer-based questions have a "long" hybrid sequence over tabular and textual elements, causing long-range reasoning problems. |
| Approach: | They propose a moving average-equipped fusion-in-decoder to handle long-range reasoning problems . they use FiD and EMA to combine different levels of reasoning . |
| Outcome: | Experimental results show that the proposed model increases exact matching and F1 by 1.1 and 1.7 on the blind test set. |
From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines (2026.acl-industry)
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| Challenge: | Existing methods that optimize for relevance overlook document trustworthiness . Generative information retrieval (GenIR) is a promising paradigm for retrieval tasks . |
| Approach: | They propose an Authority-aware Generative Retriever (AuthGR) that incorporates authority into GenIR. |
| Outcome: | The proposed framework improves authority and accuracy in real-world user engagement and reliability. |
PRIME: Ultra-Low-Rank Principal–Residual Model Merging (2026.findings-acl)
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Seung-Ho Lee, Kyungsu Lee, Bazarvaani Zuchi, Jeongmin Ahn, Insuk Seo, Donghyeon Jeon, Inho Kang, Seung-Hoon Na
| Challenge: | Existing methods for model merging have been limited by task-specific performance and task-related tasks. |
| Approach: | They propose an ultra-low-rank principal-residual model merging framework that decomposes task vector merging into two stages. |
| Outcome: | Experiments on eight natural language processing tasks show that PRIME outperforms existing models while preserving the task-specific capabilities of the original models. |
QUPID: Quantified Understanding for Enhanced Performance, Insights, and Decisions in Korean Search Engines (2025.acl-industry)
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| Challenge: | Large language models (LLMs) have been widely used for relevance assessment in information retrieval, but maintaining and updating such models is resource-intensive, limiting their feasibility in dynamic and multilingual search environments. |
| Approach: | They propose to combine a generative SLM with an embedding-based SLM to achieve higher relevance judgment accuracy while reducing computational costs. |
| Outcome: | The proposed approach outperforms state-of-the-art LLMs in relevance assessment tasks while reducing computational costs. |