Cold-start Active Learning through Self-supervised Language Modeling (2010.09535v2)

Abstract: Active learning strives to reduce annotation costs by choosing the most critical examples to label. Typically, the active learning strategy is contingent on the classification model. For instance, uncertainty sampling depends on poorly calibrated model confidence scores. In the cold-start setting, active learning is impractical because of model instability and data scarcity. Fortunately, modern NLP provides an additional source of information: pre-trained LLMs. The pre-training loss can find examples that surprise the model and should be labeled for efficient fine-tuning. Therefore, we treat the LLMing loss as a proxy for classification uncertainty. With BERT, we develop a simple strategy based on the masked LLMing loss that minimizes labeling costs for text classification. Compared to other baselines, our approach reaches higher accuracy within less sampling iterations and computation time.

• The paper proposes ALPS, a method that integrates self-supervised language modeling with active learning to address cold-start challenges.
• It employs pretrained models to estimate uncertainty for sample selection, achieving higher accuracy with fewer labeled examples.
• Experimental results across various benchmarks demonstrate reduced labeling costs and improved performance in low-resource scenarios.

Cold-start Active Learning through Self-supervised LLMing

The paper "Cold-start Active Learning through Self-supervised LLMing" explores a novel approach to address challenges in active learning, particularly in situations characterized by a lack of labeled data, known as the "cold-start" problem. Active learning traditionally relies on the availability of an initial labeled dataset to iteratively improve model performance by selecting the most informative unlabeled instances to label. This work seeks to mitigate the limitations imposed by the cold-start condition through the integration of self-supervised learning methodologies.

Core Contribution

The paper presents a method described as Active Learning with Pretrained Systems (ALPS), which leverages pretrained LLMs to guide the active learning process. The key insight is to utilize the linguistic knowledge embedded within pretrained LLMs to inform sample selection without the necessity for extensive initial labeled data. By doing so, ALPS addresses two primary objectives: reducing labeling costs and improving model performance in resource-scarce scenarios.

Methodological Framework

The authors delineate a comprehensive framework for ALPS, emphasizing:

1. Self-supervised Pretraining: Utilizing LLMs like BERT, pretrained on large corpora with self-supervised objectives, as a foundation to capture a broad understanding of language that can be harnessed in downstream tasks.
2. Sample Selection: Developing a strategy that utilizes uncertainty estimation derived from model outputs and representation learning capabilities of self-supervised models to prioritize samples for labeling.
3. Integration with Active Learning Paradigms: Embedding ALPS within existing active learning frameworks to enhance traditional query strategies.

Experimental Validation

The paper provides exhaustive experimental evaluations across diverse datasets and tasks, notably natural language understanding benchmarks. The experiments demonstrate that ALPS consistently outperforms traditional active learning baselines in cold-start conditions. Specifically, the approach achieved higher accuracy with fewer labeled instances, showcasing the efficacy of self-supervised LLMing in improving data efficiency.

Implications and Future Directions

The introduction of ALPS has several implications:

• Practically, it provides a viable solution to the often prohibitive labeling costs associated with training data-hungry models, particularly in domains where labeled data is scarce or expensive to obtain.
• Theoretically, it opens avenues for further research into the integration of self-supervised learning within other areas of machine learning, promoting methods that maximize the utility of existing unlabeled datasets.

A promising direction for future research is the exploration of ALPS in multi-modal settings or its application to more complex tasks requiring nuanced understanding, adapting the method to accommodate domain-specific challenges. Additionally, refining the uncertainty estimation mechanisms in low-resource scenarios could further enhance the applicability of the proposed methodology.

In conclusion, this paper makes a compelling case for the integration of self-supervised learning in active learning frameworks, presenting a methodologically sound and practically relevant approach to the cold-start problem. By leveraging the latent knowledge in pretrained LLMs, ALPS significantly contributes to the field of natural language processing and active learning, charting a path forward for future innovations.