Information Guided Regularization for Fine-tuning Language Models (2406.14005v2)

Abstract: The pretraining-fine-tuning paradigm has been the de facto strategy for transfer learning in modern LLMing. With the understanding that task adaptation in LMs is often a function of parameters shared across tasks, we argue that a more surgical approach to regularization needs to exist for smoother transfer learning. Towards this end, we investigate how the pretraining loss landscape is affected by these task-sensitive parameters through an information-theoretic lens. We then leverage the findings from our investigations to devise a novel approach to dropout for improved model regularization and better downstream generalization. This approach, named guided dropout, is both task & architecture agnostic and adds no computational overhead to the fine-tuning process. Through empirical evaluations, we showcase that our approach to regularization yields consistently better performance, even in scenarios of data paucity, compared to standardized baselines.

• The paper introduces guided dropout, an innovative approach using Fisher information to identify and target less sensitive parameters during fine-tuning.
• It demonstrates that selective L2 regularization improves convergence and generalization, outperforming standard dropout techniques on GLUE benchmarks.
• Empirical evaluations validate that the method enhances performance in data-scarce scenarios without additional computational overhead.

Information Guided Regularization for Fine-tuning LLMs

The paper "Information Guided Regularization for Fine-tuning LLMs" by Mandar Sharma, Nikhil Muralidhar, Shengzhe Xu, Raquib Bin Yosuf, and Naren Ramakrishnan presents a sophisticated approach to improve the fine-tuning process of LLMs (LMs) through information-guided regularization.

Overview

The authors begin by highlighting that the pretraining-fine-tuning paradigm is integral for transfer learning in contemporary LLMing. Despite significant strides, fine-tuning remains essential for adapting models to specific tasks, and ensuring effective regularization is crucial for the adaption process, particularly in data-constrained environments. Recognizing the uneven impact of different parameters on the model’s performance across tasks, the paper proposes a more differentiated (or 'surgical') regularization method.

Theoretical Foundations

Using an information-theoretic approach, the authors examine the influence of task-sensitive parameters on the LM loss landscapes. They advocate leveraging the Fisher information matrix as a proxy for the Hessian to facilitate a more computationally feasible analysis of the LM's loss landscape geometry. Their analysis identifies that a small fraction of parameters significantly influence the model's convergence and generalization properties. This insight paves the way for a novel regularization method.

Methodology

The proposed regularization approach, termed "guided dropout," leverages the understanding that certain parameters disproportionately affect the model's loss landscape. The core idea is to implement L2 regularization selectively, focusing less on critical parameters (identified via Fisher scores) and more on less significant ones, thereby minimally disturbing the model’s optimal convergence. This method is task and architecture agnostic and introduces no additional computational overhead during fine-tuning.

Empirical Evaluation

The empirical evaluation shows that guided dropout consistently outperforms standard regularization techniques across various GLUE benchmark tasks. Under data paucity conditions, guided dropout demonstrates superior generalization capabilities compared to both standard dropout and Gaussian dropout. The authors provide comprehensive performance data for tasks such as MRPC, STS-B, RTE, and CoLA, with methodologically robust random-restart experiments validating the reliability and efficacy of their approach.

Key Observations

1. Loss Landscape Analysis: Visualizing the loss landscapes of BERT models, the authors observe that parameters with high Fisher scores lead to sharp minimizers, indicating poor generalization, whereas the overall model parameters lead to wider minimizers, associated with better generalization.
2. Sub-sampling for Fisher Matrices: The paper demonstrates that reliable estimates of the Fisher information matrix can be obtained through a fractional sub-sample of the original training data, thus enabling efficient computation.
3. Layer-wise Analysis: A minority of layers in a transformer hold a significant concentration of parameters with high Fisher scores, suggesting the feasibility of targeted regularization.

Practical Implications and Future Directions

The practical implications of this research are substantial, especially in terms of improving model performance in resource-constrained environments. The task and architecture-agnostic nature of guided dropout means it can be easily integrated into existing fine-tuning workflows, benefiting a myriad of applications. Theoretically, the findings underline the importance of parameter sensitivity in shaping the LM's loss landscape, offering new avenues for research into other forms of guided regularization.

Future work could explore the application of guided dropout across various LM architectures beyond BERT, such as GPT-3 and T5. Additionally, examining the effects of non-linear scheduling for dropout probabilities might yield further performance enhancements.

Conclusion

This paper advances the understanding of regularization in LMs by introducing an information-theoretic perspective to parameter sensitivity. Guided dropout, the proposed regularization technique, leverages the Fisher information to judiciously regulate the model's parameters, enhancing generalization, particularly in data-scarce scenarios. This work provides a robust theoretical and empirical foundation for developing more effective fine-tuning strategies in AI research.