Papers by Byeongjeong Kim
Enhancing Multilingual RAG Systems with Debiased Language Preference-Guided Query Fusion (2026.findings-acl)
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| Challenge: | Existing studies show that mRAGs exhibit a perceived preference for high-resource languages, particularly English. |
| Approach: | They propose a debiased language preference metric to explicitly factor out structural priors . they propose mRAG framework that leverages monolingual alignment to optimize cross-lingual retrieval and generation. |
| Outcome: | The proposed framework outperforms baselines for English pivoting and mRAG in multiple languages. |
SAFE-SQL: Self-Augmented In-Context Learning with Fine-grained Example Selection for Text-to-SQL (2025.emnlp-main)
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| Challenge: | Text-to-SQL aims to convert natural language questions into executable SQL queries. |
| Approach: | They propose a framework that generates and filters self-augmented examples for SQL generation . using self-generated examples, they surpass previous zero-shot and few-shot frameworks . |
| Outcome: | The proposed framework surpasses the previous zero-shot and few-shot frameworks, achieving higher execution accuracy. |
Dynamic Label Name Refinement for Few-Shot Dialogue Intent Classification (2025.acl-short)
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| Challenge: | Current intent classification systems face significant challenges due to the vast number of possible intents and significant semantic overlap among similar intent classes. |
| Approach: | They propose a dynamic label refinement method that retrieves relevant examples for a test input and leverages a large language model to dynamically refine intent labels based on semantic understanding. |
| Outcome: | The proposed method resolves confusion between semantically similar intents and generates more interpretable intent labels. |
Personality Editing for Language Models through Adjusting Self-Referential Queries (2026.eacl-long)
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| Challenge: | Large Language Models (LLMs) are integral to conversational agents and content creation, but they lack robustness and require large-scale training data to achieve significant improvements in personality alignment. |
| Approach: | They propose a method that introduces adjustment queries where self-referential statements grounded in psychological constructs are treated analogously to factual knowledge to enable direct editing of personality-related responses. |
| Outcome: | The proposed method improves personality alignment across personality dimensions and requires only 12 editing samples to achieve significant improvements. |