Enriching Prompts with Definitions and Guidelines for Improved Zero-Shot Named Entity Recognition

Keskeiset käsitteet

Providing definition and guidelines in the prompt can improve the performance and robustness of instruction-tuned language models for zero-shot named entity recognition, especially on unseen entity types.

Tiivistelmä

The paper presents SLIMER, an approach for zero-shot named entity recognition (NER) that enriches the prompt with definitions and guidelines for the entity types to be extracted.

Key highlights:

• Existing models for zero-shot NER are trained on large datasets covering many entity types, often with significant overlap between training and test sets. This can limit their ability to generalize to truly unseen entity types.
• SLIMER is trained on a smaller, more focused dataset with minimal overlap between training and test entity types. To compensate for the reduced training data, the prompt is enriched with concise definitions and annotation guidelines for each entity type.
• Experiments show that the definition and guidelines help steer the model's learning, leading to better performance, faster convergence, and more stable learning, especially on never-before-seen entity types.
• Compared to state-of-the-art models, SLIMER performs comparably on out-of-domain zero-shot NER, while being trained on a much smaller and more challenging dataset.
• Ablation studies demonstrate the importance of the definition and guidelines, with the model without them requiring more training data to achieve similar performance.

Tilastot

"trailer" refers to a short promotional video that provides a preview or teaser of a forthcoming movie.
"chemical compound" refers to distinct chemical substances composed of two or more elements in fixed proportions.
"amenity" refers to services, facilities, or features that enhance the convenience, comfort, or enjoyment of a location.

Lainaukset

"Definition and Guidelines are key for flexibility to new annotation schemes."
"Guidelines potentially solve the problem of polysemous NE types where, for example, the same NE 'title' may denote film titles or nobility titles."
"Annotation guidelines may enable the labelling of never-before-seen Named Entities based on the model's ability to adhere to the provided guidelines, thus acting as a source of external knowledge for the model."

Tärkeimmät oivallukset

Show Less, Instruct More: Enriching Prompts with Definitions and Guidelines for Zero-Shot NER

by Andrew Zamai... klo arxiv.org 09-19-2024

https://arxiv.org/pdf/2407.01272.pdf

Syvällisempiä Kysymyksiä

How can the proposed approach be extended to handle a larger number of entity types without significantly increasing the inference cost?

To extend the SLIMER approach for handling a larger number of entity types while minimizing inference costs, several strategies can be employed:

Batch Processing: Instead of making individual inference calls for each entity type, SLIMER could be modified to process multiple entity types in a single batch. This would reduce the number of inference calls required, thereby lowering the overall computational cost. By structuring the prompt to include multiple entity definitions and guidelines, the model can be instructed to identify several entity types simultaneously.

Hierarchical Classification: Implementing a hierarchical classification system could allow the model to first classify the text into broader categories before narrowing down to specific entity types. This two-step process can reduce the complexity of the task and improve efficiency, as the model would only need to focus on a subset of entity types relevant to the identified category.

Dynamic Prompting: Utilizing dynamic prompting techniques where the model selects relevant definitions and guidelines based on the context of the input text can help manage the number of entity types. By only activating prompts for the most likely entity types based on the input, the model can maintain efficiency while still being capable of recognizing a wide range of entities.

Knowledge Distillation: Training a smaller, more efficient model through knowledge distillation from the larger SLIMER model can help maintain performance while reducing inference costs. The distilled model can be fine-tuned to focus on a broader set of entity types with optimized computational resources.

Incremental Learning: Implementing an incremental learning approach where the model is periodically updated with new entity types and their corresponding definitions and guidelines can help manage the complexity. This allows the model to adapt to new entities without requiring a complete retraining, thus keeping inference costs manageable.

By integrating these strategies, SLIMER can effectively scale to accommodate a larger number of entity types while maintaining efficient inference performance.

What are the potential drawbacks or limitations of relying on automatically generated definitions and guidelines, and how can they be addressed?

Relying on automatically generated definitions and guidelines presents several potential drawbacks and limitations:

Quality and Consistency: Automatically generated definitions may lack the depth and nuance that human-generated content provides. This can lead to inconsistencies in how entities are defined, which may confuse the model during inference. To address this, a review process involving human experts could be implemented to validate and refine the automatically generated content, ensuring that it meets quality standards.

Contextual Relevance: The generated definitions and guidelines may not always be contextually relevant to the specific use case or domain. This can hinder the model's ability to accurately identify entities. To mitigate this, the generation process can be tailored to consider the specific context or domain of the application, using domain-specific prompts to guide the generation of definitions and guidelines.

Overfitting to Guidelines: There is a risk that the model may become overly reliant on the provided guidelines, leading to reduced flexibility in recognizing entities that do not strictly adhere to the defined parameters. To counter this, it is essential to balance the use of guidelines with examples of edge cases and exceptions, allowing the model to learn to generalize beyond the strict definitions.

Scalability Issues: As the number of entity types increases, the effort required to generate and maintain high-quality definitions and guidelines can become burdensome. Implementing a semi-automated system where human annotators can quickly review and adjust generated content can help streamline this process, ensuring scalability without sacrificing quality.

Bias in Generation: The automatic generation process may inadvertently introduce biases based on the training data used for the language model. To address this, it is crucial to use diverse and representative datasets for training the generation model, as well as to regularly audit the generated definitions for potential biases.

By proactively addressing these limitations, the effectiveness of automatically generated definitions and guidelines can be significantly enhanced, leading to improved performance in zero-shot NER tasks.

How can the insights from this work on zero-shot NER be applied to other information extraction tasks to improve generalization to unseen concepts?

The insights gained from SLIMER's approach to zero-shot Named Entity Recognition (NER) can be effectively applied to other information extraction tasks in several ways:

Definition and Guideline Utilization: Just as SLIMER employs definitions and guidelines to enhance entity recognition, similar strategies can be applied to other information extraction tasks. For instance, in relation extraction or event extraction, providing clear definitions and contextual guidelines can help models better understand the nuances of the tasks, leading to improved performance on unseen concepts.

Instruction-Tuning Framework: The instruction-tuning framework used in SLIMER can be adapted for various information extraction tasks. By designing task-specific prompts that include relevant definitions and guidelines, models can be better aligned with the desired outputs, enhancing their ability to generalize to new and unseen data.

Leveraging Few-Shot Learning: The principle of training on a limited number of examples, as demonstrated in SLIMER, can be extended to other tasks. By focusing on few-shot learning techniques, models can be trained to recognize patterns and extract information from minimal data, which is particularly useful in domains where labeled data is scarce.

Dynamic Adaptation to New Concepts: The ability to dynamically generate definitions and guidelines can be applied to other information extraction tasks to facilitate the adaptation to new concepts. For example, in sentiment analysis, guidelines can help the model understand the context of sentiment expressions, improving its ability to generalize to new phrases or expressions that were not present in the training data.

Cross-Domain Generalization: The strategies employed in SLIMER for reducing overlap between training and test sets can inform approaches in other information extraction tasks. By ensuring that models are trained on diverse datasets with minimal overlap, they can be better equipped to generalize across different domains and contexts, thus enhancing their robustness.

Evaluation Metrics and Benchmarking: The evaluation metrics and benchmarking strategies used in SLIMER can be adapted for other information extraction tasks. By establishing clear metrics for performance evaluation, researchers can better assess the generalization capabilities of models on unseen concepts, leading to more effective model development.

By applying these insights, researchers and practitioners can enhance the performance of various information extraction tasks, leading to more robust models capable of generalizing to unseen concepts and improving overall information retrieval processes.