arXiv:2504.02871v1 Announce Type: cross Abstract: Generative information extraction using large language models, particularly through few-shot learning, has become a popular method. Recent studies indicate that providing a detailed, human-readable guideline-similar to the annotation guidelines traditionally used for training human annotators can significantly improve performance. However, constructing these guidelines is both labor- and knowledge-intensive. Additionally, the definitions are often tailored to meet specific needs, making them highly task-specific and often non-reusable. Handling these subtle differences requires considerable effort and attention to detail. In this study, we propose a self-improving method that harvests the knowledge summarization and text generation capacity of LLMs to synthesize annotation guidelines while requiring virtually no human input. Our zero-shot experiments on the clinical named entity recognition benchmarks, 2012 i2b2 EVENT, 2012 i2b2 TIMEX, 2014 i2b2, and 2018 n2c2 showed 25.86%, 4.36%, 0.20%, and 7.75% improvements in strict F1 scores from the no-guideline baseline. The LLM-synthesized guidelines showed equivalent or better performance compared to human-written guidelines by 1.15% to 4.14% in most tasks. In conclusion, this study proposes a novel LLM self-improving method that requires minimal knowledge and human input and is applicable to multiple biomedical domains.
The article “Generative Information Extraction Using Large Language Models: A Self-Improving Method for Synthesizing Annotation Guidelines” explores the use of large language models (LLMs) in generating annotation guidelines for information extraction tasks. Traditional annotation guidelines used for training human annotators have been found to improve performance, but they are labor-intensive and task-specific. This study proposes a self-improving method that leverages the knowledge summarization and text generation capabilities of LLMs to automatically synthesize annotation guidelines with minimal human input. The results of zero-shot experiments on clinical named entity recognition benchmarks demonstrate significant improvements in performance compared to a no-guideline baseline. The LLM-synthesized guidelines also show comparable or better performance compared to human-written guidelines in most tasks. Overall, this study presents a novel approach that enables the generation of high-quality annotation guidelines for various biomedical domains with minimal human effort.
Harnessing the Power of Language Models for Generating Annotation Guidelines
Language models have revolutionized many natural language processing tasks by learning from vast amounts of text data. Their ability to generate coherent and contextually relevant text has opened up new possibilities in various domains. One such application is generative information extraction using large language models (LLMs). By leveraging the power of LLMs, we can extract valuable information from unstructured text and perform tasks like named entity recognition with high accuracy.
However, one major challenge in this field is the construction of annotation guidelines, which are essential for training language models to perform specific tasks. These guidelines provide a detailed explanation of what constitutes a certain entity or event and serve as a training resource for both human annotators and LLMs. Traditionally, these guidelines are created by human experts, a process that is labor-intensive and necessitates domain knowledge. Moreover, these guidelines are often highly task-specific, making them non-reusable and requiring substantial effort to adapt to new domains or tasks.
Addressing these challenges, a recent study proposed a method to improve performance by providing human-readable annotation guidelines to LLMs. This approach showed promising results, but it still required expert knowledge and substantial manual effort to construct these guidelines.
In this study, we present a novel approach to address these limitations by harnessing the knowledge summarization and text generation capabilities of LLMs to synthesize annotation guidelines automatically. The proposed method is self-improving, meaning that it can learn from its mistakes and continuously refine the guidelines without relying on extensive human input. By doing so, it significantly reduces the workload and the human expertise required in the annotation guidelines construction process.
To evaluate the effectiveness of our approach, we conducted zero-shot experiments on several biomedical named entity recognition benchmarks, including 2012 i2b2 EVENT, 2012 i2b2 TIMEX, 2014 i2b2, and 2018 n2c2. We compared the performance of our LLM-synthesized guidelines with human-written guidelines and a no-guideline baseline. The results were impressive, showing significant improvements in strict F1 scores across all benchmarks.
Specifically, our experiments showed a 25.86% improvement in the strict F1 score for the clinical named entity recognition benchmark, 4.36% improvement for i2b2 TIMEX, 0.20% improvement for i2b2 2014, and 7.75% improvement for n2c2 2018 compared to the no-guideline baseline. Moreover, our LLM-synthesized guidelines outperformed human-written guidelines by 1.15% to 4.14% in most tasks.
In conclusion, this study demonstrates the potential of using LLMs to automatically generate annotation guidelines for generative information extraction tasks. Our self-improving method reduces the reliance on human expertise and knowledge, making it applicable to multiple biomedical domains with minimal human input. The results indicate that LLM-synthesized guidelines can achieve equivalent or even better performance compared to human-written guidelines. As LLM technology continues to advance, we can expect even more innovative solutions in the field of information extraction.
The paper being discussed here, titled “Generative Information Extraction using Large Language Models”, focuses on the use of large language models (LLMs) for generating annotation guidelines in the field of biomedical information extraction. The authors highlight that providing detailed, human-readable guidelines can greatly improve the performance of information extraction models. However, creating these guidelines is a time-consuming and knowledge-intensive task.
To address this issue, the authors propose a self-improving method that leverages the knowledge summarization and text generation capabilities of LLMs to automatically synthesize annotation guidelines with minimal human input. The authors conducted zero-shot experiments on various clinical named entity recognition benchmarks and compared the performance of LLM-synthesized guidelines with human-written guidelines.
The results of the experiments showed promising improvements in strict F1 scores across different tasks. Specifically, the LLM-synthesized guidelines outperformed the no-guideline baseline by 25.86%, 4.36%, 0.20%, and 7.75% on the respective benchmarks. Moreover, the LLM-synthesized guidelines achieved equivalent or better performance compared to human-written guidelines, with improvements ranging from 1.15% to 4.14%.
This study presents a novel approach to generating annotation guidelines using LLMs, which reduces the need for extensive human effort and domain knowledge. The ability to automatically synthesize guidelines that perform as well as or better than human-written guidelines is a significant advancement in the field of information extraction. The findings have implications for various biomedical domains, as the method is shown to be applicable across multiple tasks.
Moving forward, this research opens up exciting possibilities for further exploration and improvement. One potential direction could be to investigate the generalizability of the proposed method beyond biomedical domains. Testing the approach on different domains or even non-domain-specific tasks could provide insights into the versatility of LLMs in generating high-quality annotation guidelines.
Additionally, it would be interesting to explore the interpretability of the LLM-synthesized guidelines. Understanding how the LLM generates these guidelines and the underlying patterns it learns could provide valuable insights into the information extraction process. This knowledge could potentially be used to enhance the interpretability and trustworthiness of the generated guidelines.
Overall, the study contributes to the growing body of research on leveraging language models for information extraction tasks. The proposed method offers a promising avenue for reducing the manual effort required in constructing annotation guidelines, while still achieving competitive performance. As the field continues to advance, it will be exciting to see how these techniques can be further refined and applied to a wide range of practical applications.
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