Papers by Zijin Hong
Towards Better Question Generation in QA-based Event Extraction (2024.findings-acl)
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| Challenge: | True. True. EE aims to extract event-related information from unstructured texts. |
| Approach: | They propose a reinforcement learning method that evaluates the quality of a question and provides clear guidance to QA models. |
| Outcome: | The proposed method generates generalizable, high-quality, and context-dependent questions and provides clear guidance to QA models. |
LLMEdgeRefine: Enhancing Text Clustering with LLM-Based Boundary Point Refinement (2024.emnlp-main)
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| Challenge: | LLMEdgeRefine is an iterative clustering method enhanced by large language models . existing clustering methods struggle with domain-specific fine-tuning and outliers . |
| Approach: | They propose an iterative clustering method enhanced by large language models focusing on edge points refinement . authors propose to use LLMs to iterate clusters and iterating to improve semantic coherence . |
| Outcome: | The proposed method outperforms state-of-the-art methods and offers robustness, adaptability, and cost-efficiency for diverse text clustering applications. |
Knowledge-to-SQL: Enhancing SQL Generation with Data Expert LLM (2024.findings-acl)
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| Challenge: | Existing methods for Generating accurate SQL queries for user questions rely on the capability of large language models (LLMs) however, some knowledge is not explicitly included in the database schema and user question or has been learned by LLMs. |
| Approach: | They propose a Knowledge-to-SQL framework that employs tailored Data Expert LLM (DELLM) to provide helpful knowledge for all text-to SQL models. |
| Outcome: | The proposed framework improves the state-of-the-art approaches for text-to-SQL tasks by leveraging a data expert LLM (DELLM) to provide useful knowledge for all text- to-SqL models. |