Papers by Jong C. Park
Does Rationale Quality Matter? Enhancing Mental Disorder Detection via Selective Reasoning Distillation (2025.findings-acl)
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| Challenge: | incorporating clinical symptom information into a model enhances domain expertise, improving its detection and interpretation performance. large language models are effective for generating explanatory rationales, but inconsistencies in relevance and domain alignment of LLM-generated rationale are challenging. |
| Approach: | They propose a framework that fine-tunes smaller language models with rationales exhibiting high domain relevance . they propose 'quality-focused' approach that selects rationale based on their alignment with clinical reasoning . |
| Outcome: | The proposed framework improves mental health detection and interpretation performance by ensuring high-quality rationales with domain relevance. |
Beyond Static Benchmarks: Synthesizing Harmful Content via Persona-based Simulation for Robust Evaluation (2026.acl-long)
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| Challenge: | Existing static benchmarks for harmful content detection face limitations in scalability and diversity. |
| Approach: | They propose a framework for synthesizing harmful content using persona-guided large language model agents. |
| Outcome: | The proposed framework achieves a high success rate in harmful generation tests across multiple detection systems. |
GeezSwitch: Language Identification in Typologically Related Low-resourced East African Languages (2022.lrec-1)
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| Challenge: | Low-resourced languages with similar typologies are often confused with each other in real-world applications such as machine translation, affecting the user’s experience. |
| Approach: | They propose to build a dataset for five typologically and phylogenetically related low-resourced East African languages using the Ge’ez script as a writing system. |
| Outcome: | The proposed dataset is built automatically from selected data sources, but also performed a manual evaluation to assess its quality. |
Lossless Acceleration of Large Language Models with Hierarchical Drafting based on Temporal Locality in Speculative Decoding (2025.findings-naacl)
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Sukmin Cho, Sangjin Choi, Taeho Hwang, Jeongyeon Seo, Soyeong Jeong, Huije Lee, Hoyun Song, Jong C. Park, Youngjin Kwon
| Challenge: | Existing methods for drafting and verifying tokens require significant fine-tuning or have inconsistent performance across tasks. |
| Approach: | They propose a lossless drafting approach that organizes various token sources into multiple databases in a hierarchical framework based on temporal locality. |
| Outcome: | The proposed method outperforms existing database drafting methods on Spec-Bench using 7B and 13B parameters. |
Database-Augmented Query Representation for Information Retrieval (2025.emnlp-main)
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| Challenge: | Information retrieval models that aim to search for documents relevant to a query have shown multiple successes, but the query from the user is oftentimes short, which challenges the retrievers to correctly fetch relevant documents. |
| Approach: | They propose a database-augmented Query representation framework which augments the query with various (query-related) metadata across multiple tables. |
| Outcome: | The proposed framework significantly improves overall retrieval performance over baselines. |
EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation (2025.findings-acl)
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| Challenge: | Current retrieval-augmented generation systems struggle when retrieval models fail to rank the most relevant documents . existing extractive methods reduce latency but rely on independent, non-adaptive sentence selection . |
| Approach: | They introduce an extractive context compression framework that enhances retrieval-augmented generation in question answering. |
| Outcome: | EXIT surpasses existing compression methods and uncompressed baselines in QA accuracy . the framework reduces inference time and token count while preserving contextual dependencies . |
An Efficient Gloss-Free Sign Language Translation Using Spatial Configurations and Motion Dynamics with LLMs (2025.naacl-long)
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| Challenge: | Existing methods for sign language translation rely on glosses, which are written representations of signs. |
| Approach: | They propose a new LLM-based SLT framework that uses off-the-shelf visual encoders to extract spatial and motion features from sign videos. |
| Outcome: | The proposed framework captures spatial configurations and motion dynamics in sign language without domain-specific tuning. |
Social Dynamics as Critical Vulnerabilities that Undermine Objective Decision-Making in LLM Collectives (2026.acl-long)
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| Challenge: | Large language model (LLM) agents are increasingly acting as human delegates in multi-agent environments, where a representative agent integrates diverse peer perspectives to make a final decision. |
| Approach: | They define four key phenomena—social conformity, perceived expertise, dominant speaker effect, and rhetorical persuasion—and manipulate the number of adversaries, relative intelligence, argument length, and argumentative styles. |
| Outcome: | The results show that the reliability of the representative agent is undermined by the social context of its network. |