Show Less, Instruct More: Enriching Prompts with Definitions and Guidelines for Zero-Shot NER
(2407.01272)Abstract
Recently, several specialized instruction-tuned LLMs for Named Entity Recognition (NER) have emerged. Compared to traditional NER approaches, these models have strong generalization capabilities. Existing LLMs mainly focus on zero-shot NER in out-of-domain distributions, being fine-tuned on an extensive number of entity classes that often highly or completely overlap with test sets. In this work instead, we propose SLIMER, an approach designed to tackle never-seen-before named entity tags by instructing the model on fewer examples, and by leveraging a prompt enriched with definition and guidelines. Experiments demonstrate that definition and guidelines yield better performance, faster and more robust learning, particularly when labelling unseen Named Entities. Furthermore, SLIMER performs comparably to state-of-the-art approaches in out-of-domain zero-shot NER, while being trained on a reduced tag set.
Overview
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The paper introduces SLIMER, a novel approach to enhance zero-shot Named Entity Recognition (NER) by using enriched prompts with definitions and annotation guidelines, leveraging LLMs.
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SLIMER employs prompt enrichment techniques that include definitions and annotation directives, allowing the model to perform with limited data and showcasing strong generalization to unseen named entity (NE) tags.
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Extensive experimental validations on standard benchmarks and novel datasets reveal that SLIMER can achieve state-of-the-art performance with fewer examples, highlighting its efficiency and practicality in dynamic domains.
Enriching Prompts for Zero-Shot Named Entity Recognition (NER): The SLIMER Approach
The paper, titled "Show Less, Instruct More: Enriching Prompts with Definitions and Guidelines for Zero-Shot NER," introduces SLIMER, a novel approach to improving zero-shot NER using enriched prompts. The focus is on enhancing the LLMs' ability to handle unseen named entity (NE) tags by instructing the model with fewer examples but leveraging detailed prompts enriched with definitions and annotation guidelines.
Context and Relevance
NER is a fundamental task in NLP and crucially impacts Information Extraction pipelines. Traditional NER often suffers from poor generalization, especially in out-of-domain settings and with unseen NE tags. Contemporary LLMs, through In-Context Learning, have exhibited strong zero-shot capabilities. Nevertheless, challenges remain, particularly when dealing with unseen entity categories. SLIMER addresses these challenges by instructing models through enriched prompts, emphasizing definitions and guidelines to improve performance on unseen NEs.
Methodological Contributions
SLIMER distinguishes itself by integrating definitions and guidelines within its prompts, thus enhancing the model's instruction without extensive fine-tuning. Key methodological aspects include:
Prompt Enrichment:
- Definitions: Brief sentences describing the NE tag.
- Guidelines: Annotation directives to ensure alignment with specific annotation schemes and improve handling of edge cases.
Training with Limited Data:
- Utilizing a reduced training set consisting of only 5 examples per entity class.
- Careful selection of training NEs to minimize overlap with the test sets, ensuring more rigorous zero-shot evaluation.
Automatic Guidelines Generation:
- Leveraging another LLM (e.g., ChatGPT) to generate definitions and guidelines automatically, reducing the human effort required for this step.
Experimental Validation
The paper validates SLIMER through extensive experiments on standard zero-shot NER benchmarks such as MIT and CrossNER. Additional validation is performed on the BUSTER dataset, characterized by novel NE tags in the financial domain. Several comparative analyses include the following key observations:
Performance on Out-of-Domain NER:
- SLIMER demonstrates competitive performance compared to state-of-the-art models, despite being trained on a fraction of the data and a limited number of entity classes.
- Detailed prompts with definitions and guidelines significantly enhance model accuracy, improving stability and learning consistency.
Performance on Never-Seen-Before NEs:
- SLIMER shows strong generalization capabilities, outperforming existing models on benchmarks like BUSTER, which includes NEs not encountered during training.
- The enriched prompts help the model better understand and label these novel entities.
Training Data Analysis:
- The authors investigate the impact of increasing the number of entity types and training examples on model performance.
- Findings indicate that most performance gains are achievable with relatively few instructed instances, reinforcing the efficiency of the SLIMER methodology.
Practical and Theoretical Implications
Practically, SLIMER offers a scalable solution for zero-shot NER with enriched prompts, reducing the need for vast annotated datasets. This approach can be particularly beneficial in dynamic domains where new entities frequently emerge. The adoption of automatic guideline generation also presents a practical advantage by minimizing human annotation efforts, making the system more adaptable and easier to scale.
Theoretically, SLIMER bridges the gap between model instruction and generalization. By meticulously crafting prompt content, it highlights the importance of high-quality, informative prompts in zero-shot learning scenarios. This approach paves the way for future research on semantic enrichment of prompts and its broader implications in various NLP tasks.
Future Directions
The paper suggests several directions for future research:
Scaling to Larger Entity Sets:
- Investigate methods to optimize instruction-tuning models for larger sets of NEs, ensuring scalability without compromising performance.
Generalization to Other Information Extraction Tasks:
- Extending the SLIMER approach to other tasks in Information Extraction, exploring the benefits of enriched prompts across different domains.
Hybrid Instruction Schemes:
- Combining human-curated guidelines with automated generations, refining the instruction protocol to further enhance model efficacy and adaptability.
In conclusion, SLIMER exemplifies a significant advancement in zero-shot NER by showing that enriched prompts with definitions and guidelines can effectively improve model generalization, particularly in handling unseen NEs. This approach sets a precedent for further exploration into the fine-tuning of prompt engineering and its broader applications in the NLP landscape.
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