Vygotsky Distance: Measure for Benchmark Task Similarity (2402.14890v2)

Abstract: Evaluation plays a significant role in modern natural language processing. Most modern NLP benchmarks consist of arbitrary sets of tasks that neither guarantee any generalization potential for the model once applied outside the test set nor try to minimize the resource consumption needed for model evaluation. This paper presents a theoretical instrument and a practical algorithm to calculate similarity between benchmark tasks, we call this similarity measure "Vygotsky distance". The core idea of this similarity measure is that it is based on relative performance of the "students" on a given task, rather that on the properties of the task itself. If two tasks are close to each other in terms of Vygotsky distance the models tend to have similar relative performance on them. Thus knowing Vygotsky distance between tasks one can significantly reduce the number of evaluation tasks while maintaining a high validation quality. Experiments on various benchmarks, including GLUE, SuperGLUE, CLUE, and RussianSuperGLUE, demonstrate that a vast majority of NLP benchmarks could be at least 40% smaller in terms of the tasks included. Most importantly, Vygotsky distance could also be used for the validation of new tasks thus increasing the generalization potential of the future NLP models.

• The paper introduces Vygotsky distance as a novel metric for measuring task similarity based on relative model performance across multiple NLP benchmarks.
• It employs weighted undirected graphs and minimum spanning trees to reveal redundant tasks, showing that up to 50% of tasks may be unnecessary.
• The approach facilitates benchmark compression by accurately predicting performance on untested tasks, potentially reducing benchmark size by up to 40%.

Evaluating Task Similarity in NLP Benchmarks with Vygotsky Distance

Introduction

The field of NLP is increasingly populated with large foundational models requiring rigorous evaluation across diverse benchmarks. Traditional approaches to model evaluation often involve assessing performance over a wide range of tasks, assumed to provide a well-rounded view of a model's capabilities and generalizability. However, this extensive evaluation methodology is not only resource-intensive but detracts from the focus on developing methods that accurately gauge a model's generalization potential. This paper introduces a novel theoretical and practical framework, termed "Vygotsky distance," aimed at calculating the similarity between benchmark tasks based on the relative performance of models, rather than the inherent characteristics of the tasks themselves. The insights garnered from applying Vygotsky distance to various benchmarks could significantly streamline the evaluation process of NLP models by identifying reducible redundancy within benchmarks.

Benchmarks Graph Representation

The core of the paper revolves around the innovative depiction of benchmarks as weighted undirected graphs, where nodes represent individual tasks, and edges denote the dissimilarity in model performance across these tasks. This graphical representation leverages Vygotsky distance to evaluate task similarity, distilling benchmarks into a more manageable form without sacrificing the quality of model evaluation. Through the analysis of minimum weight spanning trees obtained from benchmark graphs, the paper uncovers structural properties and redundant tasks, revealing that a substantial portion of benchmark tasks (up to 50%) could be considered superfluous in evaluating model performance. This finding is not only significant in reducing the computational cost associated with model evaluation but also in refining the focus of benchmarks towards tasks that genuinely contribute to understanding a model's generalization capabilities.

Benchmark Compression

The practical application of Vygotsky distance extends to a method for benchmark compression. By distinguishing between "public" and "private" subsets of tasks within benchmarks, the paper presents an algorithm capable of predicting model performance on untested tasks with high accuracy, based on model outcomes on a select subset of the benchmark. This predictive approach underlines the feasibility of substantially reducing the size of benchmarks—by up to 40%—while retaining the ability to accurately estimate model generalization. This benchmark compression strategy is not only efficient but also paves the way for a more targeted and meaningful evaluation of NLP models.

Implications and Future Developments

The implications of introducing Vygotsky distance as a measure of task similarity are far-reaching. Theoretically, it provides a novel lens through which the similarity of tasks within benchmarks can be systematically assessed, moving beyond subjective categorizations of task types. Practically, the ability to compress benchmarks without losing predictive power over model evaluation promises significant improvements in the efficiency of model development cycles, especially in industrial contexts where rapid testing and iteration are crucial. Looking forward, this work suggests a new direction in benchmark development focused on maximizing the uniqueness and value of included tasks, potentially guiding the creation of benchmarks that better capture the multifaceted nature of language understanding.

Conclusion

In summary, the development and application of Vygotsky distance represent a significant step forward in the evaluation of NLP models. By focusing on the relative performance of models across tasks, this work proposes a more rational and efficient approach to benchmark construction and utilization. The potential to reduce benchmark size while maintaining or even improving the assessment of model generalization addresses both practical and theoretical challenges in the field. As the community continues to evolve and grow, tools such as Vygotsky distance will be vital in ensuring that the benchmarks keep pace, accurately reflecting progress and guiding future research directions in NLP.