Learning with Less: Knowledge Distillation from Large Language Models via Unlabeled Data (2411.08028v1)

Abstract: In real-world NLP applications, LLMs offer promising solutions due to their extensive training on vast datasets. However, the large size and high computation demands of LLMs limit their practicality in many applications, especially when further fine-tuning is required. To address these limitations, smaller models are typically preferred for deployment. However, their training is hindered by the scarcity of labeled data. In contrast, unlabeled data is often readily which can be leveraged by using LLMs to generate pseudo-labels for training smaller models. This enables the smaller models (student) to acquire knowledge from LLMs(teacher) while reducing computational costs. This process introduces challenges, such as potential noisy pseudo-labels. Selecting high-quality and informative data is therefore critical to enhance model performance while improving the efficiency of data utilization. To address this, we propose LLKD that enables Learning with Less computational resources and less data for Knowledge Distillation from LLMs. LLKD is an adaptive sample selection method that incorporates signals from both the teacher and student. Specifically, it prioritizes samples where the teacher demonstrates high confidence in its labeling, indicating reliable labels, and where the student exhibits a high information need, identifying challenging samples that require further learning. Our comprehensive experiments show that LLKD achieves superior performance across various datasets with higher data efficiency.

An Analysis of "Learning with Less: Knowledge Distillation from LLMs via Unlabeled Data"

The paper "Learning with Less: Knowledge Distillation from LLMs via Unlabeled Data" addresses the computational challenges posed by LLMs and proposes a framework for knowledge distillation (KD) that leverages unlabeled data. This approach enables smaller, more efficient models to inherit the capabilities of LLMs without the prohibitive resource requirements typically associated with LLM deployment. The authors propose a novel system called LLKD (Learning with Less for Knowledge Distillation), which employs adaptive sample selection mechanisms to improve data efficiency and computational resource utilization.

Methodology

The LLKD framework is built on a teacher-student paradigm where the teacher is a pre-trained LLM and the student is a smaller, task-specific model. Key to this approach is the use of pseudo-labels generated by the teacher model from abundant unlabeled data. The main challenge with this method lies in the potential noisiness of these pseudo-labels. The authors tackle this problem by developing an adaptive sample selection strategy that prioritizes samples showing high teacher confidence and high student uncertainty.

For measuring the quality of pseudo-labels, the proposed method considers the teacher's confidence in its predictions. The data considered most informative for the student's learning process are those with high uncertainty from the student model's perspective. These dual conditions create a dynamic thresholding technique, implemented at each training step based on both teacher and student models' predictions, that enables the selection of samples beneficial for robust model training.

Empirical Results

The authors validate their approach through extensive experiments on five datasets across various domains including medical abstracts, social media data, and professional biographies. The results indicate that LLKD consistently outperforms traditional KD methods as well as recent semi-supervised learning techniques. Notably, LLKD achieves superior accuracy and macro-F1 scores with significantly reduced training data. For instance, on the PubMed-RCT-20k dataset, the LLKD framework achieves a substantial improvement in macro-F1 score while requiring only 3.7% of the training data compared to baseline methods, demonstrating remarkable data efficiency without sacrificing performance.

Discussion and Implications

The research presented showcases the potential of a refined KD process that leverages the abundant availability of unlabeled data, addressing both practical deployment challenges and theoretical advancements in the efficiency of model training. The implications extend beyond just reducing computational resource demands; they suggest pathways for the broader application of LLMs in low-resource environments and scenarios where labeled data are scarce.

From a theoretical perspective, this framework adds to the understanding of how knowledge can be effectively distilled from an LLM to a smaller model. The integration of confidence from the teacher and uncertainty from the student into the sample selection process is a strategic advancement that can be extended to other domains of machine learning and distillation scenarios.

Future Directions

Future research may explore the generalizability of LLKD across different types and architectures of LLMs, as well as its applicability to other machine learning tasks such as generation or reinforcement learning. Moreover, investigating the impact of varying the size and training settings of both the teacher and student models could yield insights into the dynamics of knowledge transfer within this framework.

Overall, the paper contributes significantly to the evolving landscape of knowledge distillation by providing an innovative approach to efficiently leverage LLMs, ultimately improving access to advanced NLP capabilities in diverse applications. The framework appears robust and adaptable, making it an intriguing candidate for further exploration in both academic research and real-world applications.