PICLe: Pseudo-Annotations for In-Context Learning in Low-Resource Named Entity Detection (2412.11923v2)

Abstract: In-context learning (ICL) enables LLMs to perform tasks using few demonstrations, facilitating task adaptation when labeled examples are hard to obtain. However, ICL is sensitive to the choice of demonstrations, and it remains unclear which demonstration attributes enable in-context generalization. In this work, we conduct a perturbation study of in-context demonstrations for low-resource Named Entity Detection (NED). Our surprising finding is that in-context demonstrations with partially correct annotated entity mentions can be as effective for task transfer as fully correct demonstrations. Based off our findings, we propose Pseudo-annotated In-Context Learning (PICLe), a framework for in-context learning with noisy, pseudo-annotated demonstrations. PICLe leverages LLMs to annotate many demonstrations in a zero-shot first pass. We then cluster these synthetic demonstrations, sample specific sets of in-context demonstrations from each cluster, and predict entity mentions using each set independently. Finally, we use self-verification to select the final set of entity mentions. We evaluate PICLe on five biomedical NED datasets and show that, with zero human annotation, PICLe outperforms ICL in low-resource settings where limited gold examples can be used as in-context demonstrations.

• The paper introduces the PICLe framework that uses LLMs to generate pseudo-annotations, effectively substituting high-quality human demonstrations in low-resource settings.
• It describes a detailed process including zero-shot annotation, clustering, sampling, and self-verification to optimize in-context learning efficiency.
• Experimental evaluations on five biomedical datasets demonstrate PICLe's ability to outperform traditional human-annotated methods in named entity detection.

Pseudo-Annotations for In-Context Learning: Enhancements in Low-Resource Named Entity Detection

The paper "PICLe: Pseudo-Annotations for In-Context Learning in Low-Resource Named Entity Detection" discusses novel approaches to enhance the adaptability of LLMs in low-resource settings, particularly in the domain of Named Entity Detection (NED). The authors propose an innovative framework named Pseudo-annotated In-Context Learning (PICLe) that leverages the capacity of LLMs to generate pseudo-annotations, which serve as effective surrogate demonstrations in in-context learning tasks.

Key Insights and Methodology

The paper begins by contextualizing the limitations of in-context learning (ICL), which, despite its potential for rapid adaptation with few demonstrations, can be highly sensitive to the choice and quality of these demonstrations. The authors conduct a perturbation paper to determine the characteristics of effective demonstrations, revealing a key insight: partially correct annotations maintain a level of efficacy on par with fully accurate annotations, provided there is substantial overlap with the target task domain.

Building on this finding, PICLe is introduced, a framework that systematically creates pseudo-annotated demonstrations by leveraging LLMs in a zero-shot capacity. This process involves several steps:

1. Zero-Shot Annotation: LLMs are utilized to provide initial labels for a pool of unlabeled data, which are then refined through a self-verification process.
2. Clustering and Sampling: The pseudo-annotated data are organized into clusters to create groups of related samples. Demonstrations are sampled from these clusters to maximize contextual and semantic relevance.
3. Independent Prediction and Aggregation: Independent predictions using each sampled data set are aggregated to form a final prediction set. A subsequent self-verification step ensures the predictions' robustness and type accuracy.

Experimental Evaluation

The efficacy of the PICLe framework was evaluated on five biomedical datasets, showcasing superior performance in contexts with extremely limited gold-standard data. Remarkably, PICLe surpassed standard ICL systems using human-annotated gold demonstrations, particularly when the availability of these high-quality annotations was restricted. The authors demonstrate that PICLe enhances the adaptability of LLMs to detect named entities across diverse domains without requiring extensive manual annotation efforts.

Implications and Future Directions

The findings present significant implications for the utilization of LLMs in specialized, data-constrained environments such as biomedical information extraction. By demonstrating the applicability of unconventional annotation methods and leveraging existing model capabilities, the PICLe framework sets a direction for future research focused on minimizing human involvement in data annotation—a perennial bottleneck in machine learning.

Future research may explore refining the pseudo-annotation process, integrating additional context-sensitive verification mechanisms, and expanding the framework's application across other structured prediction tasks beyond NED. Furthermore, a deeper exploration into the optimal clustering mechanism and sampling strategies could yield further improvements in the framework's predictive performance.

This paper offers a robust approach to enhancing the functionality and efficiency of ICL systems, emphasizing the potential of intelligently crafted pseudo-annotations to uphold performance in low-resource scenarios. The strategic advancement in using LLMs for pseudo-label generation reflects a promising shift towards more sustainable and scalable AI applications in resource-constrained environments.