Papers by Yongchan Chun
Enhancing Automatic Term Extraction with Large Language Models via Syntactic Retrieval (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have improved IE, but their potential for ATE has not been explored. |
| Approach: | They propose a retrieval-based prompting strategy that selects demonstrations according to syntactic rather than semantic similarity in a few-shot setting. |
| Outcome: | The proposed method improves performance on three specialized ATE benchmarks. |
KoLEG: On-the-Fly Korean Legal Knowledge Editing with Continuous Retrieval (2025.findings-emnlp)
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Jaehyung Seo, Dahyun Jung, Jaewook Lee, Yongchan Chun, Dongjun Kim, Hwijung Ryu, Donghoon Shin, Heuiseok Lim
| Challenge: | a recent study shows that Korean legal knowledge is subject to frequent temporal updates driven by societal needs and government policies. |
| Approach: | They propose a Korean Legal knowledge editing framework enhanced with continuous retrieval . they employ an Editing-Aware Learning Strategy and a LawEdit Retriever . |
| Outcome: | a new framework outperforms existing methods for updating legal knowledge in Korean . it maintains robust performance in sequential editing and is qualitatively validated by legal experts. |
Benchmark Profiling: Mechanistic Diagnosis of LLM Benchmarks (2025.emnlp-main)
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| Challenge: | Large Language Models are often judged by their scores on standard benchmarks, yet such scores often overstate real capability since they mask the mix of skills a task actually demands. |
| Approach: | They propose a diagnostic framework that decomposes benchmark performance into ten cognitively grounded abilities and computes an Ability Impact Score (AIS) AIS quantifies how much each ability contributes to a model’s success on a given benchmark. |
| Outcome: | The proposed framework decomposes performance into ten cognitively grounded abilities and computes an Ability Impact Score (AIS) that quantifies how much each ability contributes to a model’s success on a given benchmark. |
Exploring Coding Spot: Understanding Parametric Contributions to LLM Coding Performance (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated proficiency in code generation and comprehension across multiple programming languages. |
| Approach: | They propose a parameter-localized subset of LLMs that facilitates coding capabilities. |
| Outcome: | The proposed model significantly improves performance on coding tasks while preserving non-coding functionalities. |
Towards Privacy-Preserving Large Language Model: Text-free Inference Through Alignment and Adaptation (2026.acl-long)
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| Challenge: | Existing LLMs require users to submit raw text regardless of its sensitivity, resulting in substantial computational overhead and degrade model performance. |
| Approach: | They propose a new training pipeline that allows a client-side encoder to condition on k-pooled prompt embeddings instead of raw text and a server-side projection module to fine-tune the projection module and LLM on private, domain-specific data using noise-injected embeddables. |
| Outcome: | The proposed approach eliminates the need for transmitting raw prompt text while maintaining a favorable balance between privacy preservation and model utility for both clients and service providers. |