Unreasonable Effectiveness of Rule-Based Heuristics in Solving Russian SuperGLUE Tasks

TL;DR

This paper demonstrates that simple rule-based heuristics can effectively solve Russian SuperGLUE tasks, revealing vulnerabilities in the benchmark and questioning the true language understanding of top models.

Contribution

The study shows that Russian SuperGLUE datasets are susceptible to shallow heuristics, and offers recommendations to improve the benchmark's robustness and representativeness.

Findings

Simple rules outperform or match advanced models on RSG tasks.

Russian SuperGLUE datasets contain exploitable statistical cues.

Shallow heuristics significantly influence top model performance.

Abstract

Leader-boards like SuperGLUE are seen as important incentives for active development of NLP, since they provide standard benchmarks for fair comparison of modern language models. They have driven the world's best engineering teams as well as their resources to collaborate and solve a set of tasks for general language understanding. Their performance scores are often claimed to be close to or even higher than the human performance. These results encouraged more thorough analysis of whether the benchmark datasets featured any statistical cues that machine learning based language models can exploit. For English datasets, it was shown that they often contain annotation artifacts. This allows solving certain tasks with very simple rules and achieving competitive rankings. In this paper, a similar analysis was done for the Russian SuperGLUE (RSG), a recently published benchmark set and leader-board for Russian natural language understanding. We show that its test datasets are vulnerable to shallow heuristics. Often approaches based on simple rules outperform or come close to the results of the notorious pre-trained language models like GPT-3 or BERT. It is likely (as the simplest explanation) that a significant part of the SOTA models performance in the RSG leader-board is due to exploiting these shallow heuristics and that has nothing in common with real language understanding. We provide a set of recommendations on how to improve these datasets, making the RSG leader-board even more representative of the real progress in Russian NLU.