Papers by Hwichan Kim
A Single Linear Layer Yields Task-Adapted Low-Rank Matrices (2024.lrec-main)
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| Challenge: | Low-Rank Adaptation (LoRA) is a parameter-efficient fine-tuning method that updates initial weight matrix W0 with a delta matrix W . |
| Approach: | They propose a method that updates initial weight matrix W0 with a delta matrix W consisting of two low-rank matrices A and B. |
| Outcome: | The proposed method maintains a performance on par with LoRA despite the fact that the trainable parameters of CondLoRA are fewer than those of LoRA. |
Zero-shot North Korean to English Neural Machine Translation by Character Tokenization and Phoneme Decomposition (2020.acl-srw)
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| Challenge: | a limited number of North Korean to English translation models have been developed . a zero-shot approach is proposed to train a neural machine translation model using South Korean data . |
| Approach: | They propose a method to tokenize South Korean input sentences and decompose them into phonemes. |
| Outcome: | The proposed method improves the BLEU scores by +1.01 points compared with the baseline . the proposed method can learn North Korean to English translation and improve the linguistic accuracy. |
Learning How to Translate North Korean through South Korean (2022.lrec-1)
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| Challenge: | Existing NLP systems cannot properly handle North Korean inputs, despite limited data . Several NLP researchers have been working on the Korean language . |
| Approach: | They propose to manually create evaluation data for automatic alignment and machine translation, and investigate automatic alignment methods suitable for North Korean. |
| Outcome: | The proposed model trained by North Korean bilingual data significantly boosts translation accuracy compared to existing South Korean models in zero-shot settings. |
Pruning Multilingual Large Language Models for Multilingual Inference (2024.findings-emnlp)
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| Challenge: | Multilingual large language models (MLLMs) demonstrate better zeroshot learning performance in non-English languages compared to large language model trained on English-dominant data. |
| Approach: | They propose a pruning approach to prune large language models using bilingual sentence pairs from English and other languages to enhance their performance in non-English language. |
| Outcome: | The proposed pruning strategy enhances the MLLMs’ performance in non-English language. |
Does Masked Language Model Pre-training with Artificial Data Improve Low-resource Neural Machine Translation? (2023.findings-eacl)
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| Challenge: | Pre-training masked language models with artificial data has been proven beneficial for several natural language processing tasks, however, it has been less explored for neural machine translation (NMT). |
| Approach: | They pre-trained masked language models with random sequences and created artificial data mimicking token frequency information from the real world. |
| Outcome: | The results show that pre-training models with artificial data improves translation performance in low-resource situations. |
Enhancing Few-shot Cross-lingual Transfer with Target Language Peculiar Examples (2023.findings-acl)
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| Challenge: | We use multilingual mask language models to train a model with hard examples, but if no labeled data are available, they need to be created through human annotations. |
| Approach: | They devised a metric to select annotation candidates from an unlabeled data pool that efficiently enhance accuracy for few-shot cross-lingual transfer. |
| Outcome: | The proposed model improves accuracy for few-shot cross-lingual transfer in 20 languages and 6 tasks with high peculiarity examples. |
A Fair Comparison without Translationese: English vs. Target-language Instructions for Multilingual LLMs (2025.naacl-short)
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| Challenge: | Prior studies suggested that English instructions are more effective for non-English tasks . however, these studies often use datasets and instructions translated from English . |
| Approach: | They conduct a fair comparison between English and target-language instructions by eliminating translationese effects. |
| Outcome: | The results show that the advantage of adopting English instructions is not overwhelming . the results also show that instruction-following abilities are improved when using respective instructions. |