GoLLIE: Annotation Guidelines for Zero-Shot Information Extraction

Core Concepts

Large Language Models combined with annotation guidelines can improve zero-shot information extraction.

Abstract

GoLLIE proposes a model that follows annotation guidelines to enhance zero-shot information extraction. Large Language Models have struggled with Information Extraction tasks due to the complexity of annotation guidelines. GoLLIE outperforms previous attempts by fine-tuning to comply with detailed guidelines. The model leverages pre-training knowledge to extract mentions based on categories defined in the guidelines. However, challenges arise when different annotation schemas define labels differently. The ablation study shows that detailed guidelines are crucial for good results. GoLLIE introduces various training regularizations to ensure compliance with guidelines and prevent overfitting to specific datasets.

Stats

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

Quotes

"Large Language Models combined with instruction tuning have made significant progress when generalizing to unseen tasks."
"GoLLIE is able to generalize to and follow unseen guidelines, outperforming previous attempts at zero-shot information extraction."
"Current LLMs have been trained to follow instructions, but they fail to follow annotation guidelines out of the box."

Key Insights Distilled From

GoLLIE

by Osca... at arxiv.org 03-07-2024

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

Deeper Inquiries

How can GoLLIE's approach be applied beyond Information Extraction tasks

GoLLIE's approach can be applied beyond Information Extraction tasks by leveraging annotation guidelines to improve model generalization in various natural language processing (NLP) applications. For instance, in sentiment analysis, the guidelines could provide specific criteria for identifying sentiments expressed in text, leading to more accurate sentiment classification. In machine translation, guidelines could help ensure that translations are faithful to the original text by providing detailed instructions on handling nuances and idiomatic expressions. Additionally, in question answering systems, guidelines could assist in formulating precise prompts for extracting relevant information from passages.

What are potential drawbacks or limitations of relying heavily on annotation guidelines for model performance

Relying heavily on annotation guidelines for model performance may have potential drawbacks or limitations. One limitation is the dependency on human-created rules and definitions which may not always capture all variations or edge cases present in real-world data. This can lead to biases introduced through the annotations and restrict the model's ability to adapt to new scenarios outside of the annotated guidelines. Moreover, strict adherence to guidelines may limit the model's flexibility and creativity in understanding complex linguistic patterns that deviate from predefined rules.

How might the use of diverse pre-training datasets impact GoLLIE's performance on ambiguous or coarse labels

The use of diverse pre-training datasets can impact GoLLIE's performance on ambiguous or coarse labels by exposing it to a wider range of linguistic contexts and label variations. By training on diverse datasets with varying levels of granularity and ambiguity, GoLLIE can learn robust representations that generalize well across different types of labels. This exposure helps the model better understand subtle distinctions between similar labels and improves its ability to handle ambiguous entities effectively during inference. Additionally, training on diverse datasets enhances GoLLIE's adaptability when faced with novel or unseen label categories by providing a broader knowledge base for making informed predictions.