LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages (2404.02261v2)

Abstract: Low-resource languages face significant barriers in AI development due to limited linguistic resources and expertise for data labeling, rendering them rare and costly. The scarcity of data and the absence of preexisting tools exacerbate these challenges, especially since these languages may not be adequately represented in various NLP datasets. To address this gap, we propose leveraging the potential of LLMs in the active learning loop for data annotation. Initially, we conduct evaluations to assess inter-annotator agreement and consistency, facilitating the selection of a suitable LLM annotator. The chosen annotator is then integrated into a training loop for a classifier using an active learning paradigm, minimizing the amount of queried data required. Empirical evaluations, notably employing GPT-4-Turbo, demonstrate near-state-of-the-art performance with significantly reduced data requirements, as indicated by estimated potential cost savings of at least 42.45 times compared to human annotation. Our proposed solution shows promising potential to substantially reduce both the monetary and computational costs associated with automation in low-resource settings. By bridging the gap between low-resource languages and AI, this approach fosters broader inclusion and shows the potential to enable automation across diverse linguistic landscapes.

• The paper integrates GPT-4-Turbo annotations within an active learning loop to reduce labeling costs in low-resource NER tasks.
• It demonstrates cost reductions of over 42x for Bambara and 53x for isiZulu with minimal accuracy trade-offs compared to human annotations.
• The methodology uses uncertainty-based sample selection and batch processing to optimize annotation efficiency.

Leveraging LLM Annotations for Active Learning in Low-Resource Languages

Introduction

The paper "LLMs in the Loop: Leveraging LLM Annotations for Active Learning in Low-Resource Languages" proposes a novel approach to data annotation in low-resource languages. By integrating LLMs within an active learning framework, the authors aim to address the challenges posed by limited linguistic resources, high human annotation costs, and the absence of pre-existing tools. The paper focuses on Named Entity Recognition (NER) tasks, utilizing foundation models to annotate data efficiently.

Methodology

The research capitalizes on the capabilities of LLMs like GPT-4-Turbo, employing them as annotators in an active learning loop. The process involves sampling the most informative data points based on uncertainty quantification—specifically entropy-based measures—to optimize resource utilization. The strategy includes querying LLMs for annotations using a carefully designed prompt template, allowing for batch processing to minimize resource consumption.

Figure 1: Overview of our methodology. The process involves selecting the most informative samples from the training set, and querying the LLM with a pre-defined prompt template to obtain annotations. The problem-specific classifier is then trained with these queried annotations and evaluated on the unseen test set.

The methodology showcases the integration of foundation models into an active learning framework, with specific iterations aimed at expediting the annotation process. This approach promises substantial reductions in both monetary and computational costs, bridging gaps between low-resource languages and modern AI applications.

Experimental Setup and Model Selection

Several LLMs are evaluated for their annotation capacity in low-resource NER tasks, including GPT-4-Turbo, Claude 3 Opus, and others. The assessment criteria encompass the ability to adhere to output formats, label accuracy, and consistency across annotations. The primary challenge lies in generating reliable annotations without token omissions, a common issue among LLMs.

To exemplify annotation efficiency, the research relies on a subset of records from the MasakhaNER 2.0 dataset, utilizing a balanced sampling method to ensure representative samples for evaluation. The experiment's design focuses on overcoming common annotation challenges, prompting quality assurance, and minimizing token omission errors.

Results and Discussion

Through active learning iterations using experimental data from the Bambara and isiZulu datasets, the paper demonstrates improved annotation processes. While active learning with human-annotated ground truth achieves baseline performance using 20% of the dataset, LLM-generated annotations approach this benchmark with significant cost savings.

The cost analysis reveals that GPT-4-Turbo annotations offer a substantial reduction in expense—approximately 42.45 times lower for Bambara and 53.18 times lower for isiZulu compared to traditional methods. Despite minimal accuracy trade-offs compared to human annotations, the efficiency gained in cost and resource conservation is particularly relevant in low-resource settings.

Figure 2: Accuracy (without non-entities) for Bambara test set achieved by using ground truth (left) and GPT-4-Turbo annotations (right) in our active learning framework. X-axis denotes the percentage of the dataset used for active learning iteration, and red dashed line represents our baseline - simple AfroXLMR-mini training using 100~\% of the dataset without active learning.

Figure 3: Accuracy (without non-entities) for isiZulu test set achieved by using ground truth (left) and GPT-4-Turbo annotations (right) in our active learning framework. X-axis denotes the percentage of the dataset used for active learning iteration, and red dashed line represents our baseline - simple AfroXLMR-mini training using 100~\% of the dataset without active learning.

Implications and Future Work

The paper opens avenues for extending LLM application into other low-resource languages, fostering inclusivity in AI developments. It highlights the need for continued evaluation and improvement of prompt designs and annotation efficiency in LLMs. Future research could explore using more advanced models like Claude 3 Opus, which demonstrated potential for higher accuracy on smaller benchmarks but at a higher cost.

Additionally, the paper introduces a novel methodology for assessing potential data contamination within LLM training datasets, a critical step to ensure applicable results across similar linguistic domains.

Conclusion

The integration of LLMs into the active learning framework for low-resource languages offers a promising direction for cost-effective, automated data annotation in the context of NER tasks. While GPT-4-Turbo may exhibit minor accuracy discrepancies compared to human annotations, the substantial cost benefits position LLMs as viable alternatives in scenarios with constrained resources. By addressing inherent challenges such as token skipping, this approach sets a precedent for leveraging foundation models to facilitate broader AI deployment across diverse linguistic landscapes.