Papers by Honghao Gui
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. |
Schema-adaptable Knowledge Graph Construction (2023.findings-emnlp)
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| Challenge: | Existing Knowledge Graph Construction (KGC) tasks rely on static information extraction with a closed set of pre-defined schemas. |
| Approach: | They propose a static knowledge Graph Construction task that extracts entity, relation, and event based on dynamically changing schema graph without retraining. |
| Outcome: | The proposed system outperforms existing methods but still has room for improvement . it can extract entity, relation, and event based on dynamically changing schema graph without re-training . |
EasyInstruct: An Easy-to-use Instruction Processing Framework for Large Language Models (2024.acl-demos)
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Yixin Ou, Ningyu Zhang, Honghao Gui, Ziwen Xu, Shuofei Qiao, Runnan Fang, Lei Li, Zhen Bi, Guozhou Zheng, Huajun Chen
| Challenge: | Large Language Models (LLMs) have improved performance across tasks and domains . instruction tuning is a crucial technique to enhance the capabilities of LLMs - but there is no standard open-source instruction processing framework available for the community . |
| Approach: | They propose an open-source instruction tuning framework for Large Language Models that modularizes instruction generation, selection, prompting and their combination and interaction. |
| Outcome: | The proposed framework is open-source and available on Github. |
Making Language Models Better Tool Learners with Execution Feedback (2024.naacl-long)
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| Challenge: | Existing tool learning methodologies induce large language models to utilize tools indiscriminately . Existing frameworks that teach language models when and how to use tools propagate errors rather than enhance performance. |
| Approach: | They propose a framework that enables large language models to continually learn through feedback derived from tool execution. |
| Outcome: | The proposed framework can make large language models selectively use tools . it improves accuracy while enhancing insufficient tool learning, it shows . |