Papers by Seongbo Jang
Exploring Language Model’s Code Generation Ability with Auxiliary Functions (2024.findings-naacl)
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| Challenge: | Auxiliary function is a useful component to improve language model’s code generation ability, but a systematic exploration of how they affect has yet to be done. |
| Approach: | They construct a human-crafted evaluation set which contains examples of two functions where one function assists the other to examine their ability in a multifaceted way. |
| Outcome: | The proposed model is underutilized to call the auxiliary function, suggesting future directions to enhance their implementation by eliciting the supplementary function call ability encoded in the models. |
An Empirical Study of Tokenization Strategies for Various Korean NLP Tasks (2020.aacl-main)
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| Challenge: | Traditionally, tokenization is the very first step in most text processing works. |
| Approach: | They propose to use morphological segmentation followed by BPE for Korean NLP tasks . they empirically examine what is the best tokenization strategy for Korean to/from English . |
| Outcome: | The proposed approach is best for Korean to/from English machine translation and natural language understanding tasks. |
Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning (2022.acl-long)
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| Challenge: | Existing studies have shown that a pretrained language model can capture sentence similarity but there is no interpretation method for the sentence similarities. |
| Approach: | They propose a pretrained language model that captures sentence similarity between embeddings and a transport-based distance measure that leverages semantically-aligned token pairs. |
| Outcome: | The proposed framework outperforms baselines on both STS and interpretable-STS benchmarks and provides interpretation consistent with human judgement. |
KoDialogBench: Evaluating Conversational Understanding of Language Models with Korean Dialogue Benchmark (2024.lrec-main)
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| Challenge: | KoDialogBench is a benchmark designed to assess language models’ conversational capabilities in low-resource languages such as Korean. |
| Approach: | They propose a benchmark to assess language models’ conversational capabilities in Korean by collecting native Korean dialogues from public sources and translating them into diverse test datasets. |
| Outcome: | The proposed benchmark measures the conversational capabilities of language models in Korean, and shows that they can improve on previous training techniques. |