Papers by Hongbin Sun
IEPile: Unearthing Large Scale Schema-Conditioned Information Extraction Corpus (2024.acl-short)
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| Challenge: | Large Language Models exhibit a significant performance gap in Information Extraction (IE) high-quality instruction data is the vital key for enhancing LLMs' specific capabilities . |
| Approach: | They propose a bilingual (English and Chinese) IE instruction corpus that contains 0.32B tokens. |
| Outcome: | The proposed model improves the performance of LLMs for IE with zero-shot generalization. |
RACE: Retrieval-augmented Commit Message Generation (2022.emnlp-main)
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| Challenge: | Existing approaches to automatically generate commit messages are repetitive or redundant. |
| Approach: | They propose a retrieval-augmented neural commit message generation method which treats the retrieved similar commit as an exemplar and leverages it to generate an accurate commit message. |
| Outcome: | The proposed method outperforms baselines on a large dataset with five programming languages and can boost existing Seq2Seq models in commit message generation. |
CAST: Enhancing Code Summarization with Hierarchical Splitting and Reconstruction of Abstract Syntax Trees (2021.emnlp-main)
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| Challenge: | Existing methods for code summarization do not capture rich information in ASTs . existing methods are labor-intensive and time-consuming to document code with good summaries manually. |
| Approach: | They propose a model that hierarchically splits and reconstructs ASTs by a neural network . they propose to use AST embeddings and a vanilla code token encoder to generate the model . |
| Outcome: | The proposed model splits and reconstructs ASTs into subtrees and then aggregates embeddings of subtreas to get the complete AST. |