Papers by Jaewook Kang
PhoniTale: Phonologically Grounded Mnemonic Generation for Typologically Distant Language Pairs (2025.emnlp-main)
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| Challenge: | Vocabulary acquisition is a challenge for second-language learners when learning typologically distant languages such as English and Korean, where phonological and structural mismatches complicate vocabulary learning. |
| Approach: | They propose a cross-lingual mnemonic generation system that performs IPA-based phonological adaptation and syllable-aware alignment to retrieve L1 keyword sequence and uses LLMs to generate verbal cues. |
| Outcome: | The proposed system outperforms human-written and automated mnemonics in a short-term recall test with human participants and achieves quality comparable to human-writing mnms. |
CheckEval: A reliable LLM-as-a-Judge framework for evaluating text generation using checklists (2025.emnlp-main)
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| Challenge: | Existing evaluation protocols for text generation suffer from rating inconsistencies . lexical overlap-based metrics align poorly with human judgments . |
| Approach: | They propose a checklist-based evaluation framework that improves rating reliability via decomposed binary questions. |
| Outcome: | The proposed framework improves rating reliability by decomposing binary questions . it improves agreement across evaluator models by 0.45 and reduces score variance . human evaluation remains the gold standard, but it #, Equal contribution. |
What Changes Can Large-scale Language Models Bring? Intensive Study on HyperCLOVA: Billions-scale Korean Generative Pretrained Transformers (2021.emnlp-main)
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Boseop Kim, HyoungSeok Kim, Sang-Woo Lee, Gichang Lee, Donghyun Kwak, Jeon Dong Hyeon, Sunghyun Park, Sungju Kim, Seonhoon Kim, Dongpil Seo, Heungsub Lee, Minyoung Jeong, Sungjae Lee, Minsub Kim, Suk Hyun Ko, Seokhun Kim, Taeyong Park, Jinuk Kim, Soyoung Kang, Na-Hyeon Ryu, Kang Min Yoo, Minsuk Chang, Soobin Suh, Sookyo In, Jinseong Park, Kyungduk Kim, Hiun Kim, Jisu Jeong, Yong Goo Yeo, Donghoon Ham, Dongju Park, Min Young Lee, Jaewook Kang, Inho Kang, Jung-Woo Ha, Woomyoung Park, Nako Sung
| Challenge: | GPT-3 has been used to train large-scale language models on hundreds of billion scale data. |
| Approach: | They propose a Korean variant of GPT-3 that uses Korean tokens to train in-context models. |
| Outcome: | The proposed method shows state-of-the-art zero-shot and few-shot learning on downstream tasks in Korean. |
Enhancing Out-of-Distribution Detection in Natural Language Understanding via Implicit Layer Ensemble (2022.findings-emnlp)
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| Challenge: | Out-of-distribution (OOD) detection aims to discern outliers from the intended data distribution, which is crucial to maintaining high reliability and a good user experience. |
| Approach: | They propose a framework that encourages intermediate features to learn layer-specialized representations and assembles them implicitly into a single representation to absorb rich information in the pre-trained language model. |
| Outcome: | The proposed framework is significantly more effective than previous studies in intent classification and OOD datasets. |
Make LLMs See Like Investigators, Not Just Think More: The Role of Structured Analysis in Investigative Reasoning (2026.acl-long)
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| Challenge: | Criminal investigators and intelligence analysts have developed structured analytic techniques to evaluate competing hypotheses under incomplete information. |
| Approach: | They focus on the task of analyzing evidence from complex narratives and identifying the perpetrator among suspects using the MuSR murder mystery benchmark. |
| Outcome: | The PRISM framework outperforms general-purpose strategies across all models, with its effectiveness manifesting regardless of model scale. |
Navigating the Path of Writing: Outline-guided Text Generation with Large Language Models (2025.naacl-industry)
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| Challenge: | Large Language Models (LLMs) have impacted the writing process, enhancing productivity by collaborating with humans in content creation platforms. |
| Approach: | They propose a framework that uses explicit outlines to guide LLMs in generating goal-oriented, high-quality text. |
| Outcome: | The proposed approach significantly improves text quality according to evaluations by LLMs and professional writers. |
GPT3Mix: Leveraging Large-scale Language Models for Text Augmentation (2021.findings-emnlp)
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| Challenge: | Recent studies report that prompt-based direct classification eliminates the need for fine-tuning but lacks data and inference scalability. |
| Approach: | They propose a data augmentation technique that leverages large-scale language models to generate real text samples from a mixture of real samples. |
| Outcome: | The proposed method outperforms existing methods on diverse classification tasks. |
HyperT5: Towards Compute-Efficient Korean Language Modeling (2023.acl-industry)
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| Challenge: | Pretraining and fine-tuning language models is a common practice in NLP, but deploying general-purpose language models without the abundant computation or data resources is proving difficult. |
| Approach: | They propose a sequence-to-sequence language model architecture that can be more practical and compute-efficient than the decoder-oriented approach. |
| Outcome: | The proposed language model outperforms competing models in Korean benchmarks and is more efficient in low-resource settings. |
Can We Entrust Justice to AI?: How Persona Traps Contaminate Reasoning in Criminal Investigation (2026.findings-acl)
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| Challenge: | a study injected personas into neutralized murder mystery scenarios to examine reasoning stability of large language models . adrian s. gupta: if models are used to evaluate suspects, are they free from the trap of implicit bias? he says current alignment techniques focus on identity-based bias while neglecting relationship-based ones . |
| Approach: | a study systematically injected personas into neutralized murder mystery scenarios . it found that implicit bias propagation was observed across all models . authors propose stability evaluation should encompass outputs and reasoning processes . |
| Outcome: | The proposed pipeline can analyze evidence and evaluate suspects in murder mysteries . it shows that models outwardly state "that information is irrelevant to the judgment" the proposed pipeline could be extended to include reasoning processes, authors say . |