Papers by Deokhyung Kang
Why Do Multilingual Reasoning Gaps Emerge in Reasoning Language Models? (2026.findings-acl)
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| Challenge: | Recent reasoning language models (RLMs) achieve strong performance on complex reasoning tasks, yet they still exhibit a multilingual reasoning gap. |
| Approach: | They propose a strategy that incorporates an English translation into the initial reasoning trace when an understanding failure is detected. |
| Outcome: | The proposed strategy incorporates an English translation into the initial reasoning trace when an understanding failure is detected. |
Retrieval-Augmented Fine-Tuning With Preference Optimization For Visual Program Generation (2025.acl-long)
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| Challenge: | Visual programming languages (VPLs) allow users to create programs through graphical interfaces, which results in easier accessibility and widespread usage in various domains. |
| Approach: | They propose to train VPLs from user instructions using large language models . they propose to use retrieval-augmented fine-tuning to leverage repetitive use of subroutines . |
| Outcome: | The proposed method outperforms prompting-based methods for LD generation accuracy even with smaller backbone models. |
Self-Correcting Code Generation Using Small Language Models (2025.findings-emnlp)
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| Challenge: | a recent study has demonstrated that self-correction is a powerful tool for code generation, but whether it is effective for smaller models remains unexplored. |
| Approach: | They propose a method that trains small language models to maintain correct outputs while progressively correcting incorrect outputs as turns proceed. |
| Outcome: | The proposed approach improves the ability of small language models for multi-turn code correction. |
Cross-lingual Back-Parsing: Utterance Synthesis from Meaning Representation for Zero-Resource Semantic Parsing (2024.emnlp-main)
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| Challenge: | Existing approaches to extend semantic parsing (SP) beyond English are challenging due to the complex slot alignment step after translation. |
| Approach: | They propose a method to enhance cross-lingual transfer for SP by utilizing mPLMs. |
| Outcome: | The proposed method synthesizes target language utterances from source meaning representations while maintaining high slot value alignment rates. |
MiLQ: Benchmarking IR Models for Bilingual Web Search with Mixed Language Queries (2025.emnlp-main)
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| Challenge: | Information Retrieval (IR) research on mixed-language queries remains sparse and outdated. |
| Approach: | They propose a test set for mixed-language queries that is realistic and preferred by bilingual speakers. |
| Outcome: | The proposed benchmarks show that multilingual IR models perform moderately on MiLQ and inconsistently across native, English, and mixed-language queries. |
Denoising Table-Text Retrieval for Open-Domain Question Answering (2024.lrec-main)
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| Challenge: | Existing studies in table-text open-domain question answering have problems with false-positive labels in training datasets. |
| Approach: | They propose a denoised table-text retriever that discards false positives from training datasets . they integrate table-level ranking information into the retriever to assist in finding evidence . |
| Outcome: | The proposed method outperforms baselines on retrieval recall and QA tasks. |
EnSToM: Enhancing Dialogue Systems with Entropy-Scaled Steering Vectors for Topic Maintenance (2025.findings-acl)
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| Challenge: | Small large language models (sLLMs) are lightweight and efficient, but struggle to maintain topic consistency in task-oriented dialogue systems. |
| Approach: | They propose an approach to ensure topic consistency in task-oriented dialogue systems by manipulating internal activations during inference. |
| Outcome: | The proposed approach achieves significant performance gain with a relatively small data size compared to fine-tuning approaches. |