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使用大型语言模型的列属性注释Column Property Annotation using Large Language Models |
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| 课程网址: | https://videolectures.net/eswc2024_korini_property_annotation/ |
| 主讲教师: | Keti Korini |
| 开课单位: | 2024年上海世博会 |
| 开课时间: | 2024-06-17 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Column property annotation (CPA), also known as column relationship prediction, is the task of predicting the semantic relationship between two columns in a table given a set of candidate relationships. CPA annotations are used in downstream tasks such as data search, data integration, or knowledge graph enrichment. This paper explores the usage of generative large language models (LLMs) for the CPA task. We experiment with different zero-shot prompts for the CPA task which we evaluate using GPT-3.5, GPT-4, and the open-source model SOLAR. We find GPT-3.5 to be quite sensitive to variations of the prompt, while GPT-4 reaches a high performance independent of the variation of the prompt. We further explore the scenario where training data for the CPA task is available and can be used for selecting demonstrations or finetuning the model. We show that a fine-tuned GPT-3.5 model outperforms a RoBERTa model that was fine-tuned on the same data by 11% in F1. Comparing in-context learning via demonstrations and fine-tuning shows that the fine-tuned GPT-3.5 performs 9% F1 better than the same model given demonstrations. The fine-tuned GPT-3.5 model also outperforms zero-shot GPT-4 by around 2% F1 for the dataset on which is was finetuned, while not generalizing to tasks that require a different vocabulary |
| 关 键 词: | 大型语言模型; 列属性注释; 列关系预测 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2024-08-07：liyq |
| 最后编审: | 2024-08-07：liyq |
| 阅读次数: | 96 |