GoLLIE: Annotation Guidelines for Zero-Shot Information Extraction

Concepts de base

GoLLIE improves zero-shot information extraction by following annotation guidelines, outperforming previous attempts.

Résumé

GoLLIE introduces a model fine-tuned to comply with annotation guidelines, showing significant progress in zero-shot information extraction. The model leverages detailed guidelines to improve performance on unseen tasks, surpassing previous methods. By following instructions and adhering to guidelines, GoLLIE demonstrates the importance of annotation guidelines in enhancing model performance.

Large Language Models (LLMs) combined with instruction tuning have made significant progress in generalizing to unseen tasks. However, they have been less successful in Information Extraction (IE), lagging behind task-specific models. Typically, IE tasks are characterized by complex annotation guidelines that describe the task and give examples to humans. Previous attempts to leverage such information have failed, even with the largest models, as they are not able to follow the guidelines out of the box.

In this paper, GoLLIE is proposed as a model able to improve zero-shot results on unseen IE tasks by being fine-tuned to comply with annotation guidelines. Comprehensive evaluation empirically demonstrates that GoLLIE is able to generalize and follow unseen guidelines, outperforming previous attempts at zero-shot information extraction.

The ablation study shows that detailed guidelines are key for good results. Code, data, and models are publicly available on GitHub.

Stats

Movie: 63
Restaurant: 21
Politics: 20
Literature: 31
Music: 24
AI: 41
Science: 41

Citations

"Building a system that enables high-performance zero-shot information extraction remains an open challenge."
"In this work, we present GoLLIE (Guideline-following Large Language Model for IE), an LLM fine-tuned to learn how to attend to the guidelines on a small set of well-known IE tasks."

Idées clés tirées de

GoLLIE

by Osca... à arxiv.org 03-07-2024

https://arxiv.org/pdf/2310.03668.pdf

Questions plus approfondies

How can leveraging detailed annotation guidelines improve performance in other NLP tasks?

Leveraging detailed annotation guidelines can significantly improve performance in other NLP tasks by providing clear instructions to the model on how to interpret and process the data. These guidelines help in defining the task, specifying label definitions, giving examples, and outlining exceptions. By following these guidelines, models like GoLLIE can better understand the nuances of different labels and make more accurate predictions. This approach ensures that the model focuses on relevant information and avoids common pitfalls such as mislabeling or confusion between similar entities.
Detailed annotation guidelines also enhance consistency across annotations, making it easier for models to generalize to new tasks or domains. They provide a structured framework for training data creation and evaluation, leading to more reliable results. Additionally, by incorporating specific instructions into the training process, models become more robust and adaptable when faced with unseen scenarios.

How can leveraging detailed annotation guidelines improve performance in other NLP tasks?

When using large language models without compliance with specific annotation guidelines, several challenges may arise:

Misinterpretation of Labels: Large language models may struggle to accurately identify complex labels without clear guidance from detailed annotations. This could lead to misclassification of entities or events based solely on superficial characteristics rather than their actual context.

Inconsistencies in Predictions: Without adherence to specific annotation guidelines, large language models may produce inconsistent predictions across different datasets or domains. This inconsistency hampers generalization capabilities and reduces overall performance on unseen tasks.

Overfitting: Models trained without compliance with specific annotation guidelines are at risk of overfitting to certain patterns present in the training data but not reflective of real-world scenarios. This limits their ability to adapt effectively when presented with new information outside their training scope.

4Limited Understanding of Task Complexity: Detailed annotation guidelines provide essential context for understanding task complexity and variations within a dataset. Without this guidance, large language models may struggle to capture intricate relationships between entities or events accurately.
5Reduced Robustness: Failure to follow specific annotation guidelines can result in reduced robustness of large language models when dealing with noisy or ambiguous input data sets.

How can the concept of following instructions be applied beyond natural language processing?

The concept of following instructions extends beyond natural language processing (NLP) into various fields where precise task execution is crucial:
1Robotics: In robotics applications, robots need clear instructions on how to perform tasks efficiently and safely.
2Manufacturing: Assembly lines, automated machinery require precise instructions for manufacturing processes.
3Healthcare: Medical procedures, treatment protocols rely on accurate instruction-following by healthcare professionals.
4Education: Learning environments, students benefit from well-defined assignments that guide them through learning objectives
5Project Management: Teams working on projects adhere closely project plans ensure successful completion within deadlines
By emphasizing instruction-following principles outside NLP contexts we promote efficiency accuracy ensuring desired outcomes are achieved consistently across diverse industries