The Curse of Performance Instability in Analysis Datasets: Consequences, Source, and Suggestions
This addresses a critical issue for researchers and practitioners in NLP by exposing instability in widely used analysis datasets, potentially leading to more robust evaluation practices.
The paper identifies that state-of-the-art models on Natural Language Inference (NLI) and Reading Comprehension (RC) analysis sets exhibit high performance instability, which undermines the reliability of conclusions and model-selection routines, and attributes this to high inter-example correlations within the sets.
We find that the performance of state-of-the-art models on Natural Language Inference (NLI) and Reading Comprehension (RC) analysis/stress sets can be highly unstable. This raises three questions: (1) How will the instability affect the reliability of the conclusions drawn based on these analysis sets? (2) Where does this instability come from? (3) How should we handle this instability and what are some potential solutions? For the first question, we conduct a thorough empirical study over analysis sets and find that in addition to the unstable final performance, the instability exists all along the training curve. We also observe lower-than-expected correlations between the analysis validation set and standard validation set, questioning the effectiveness of the current model-selection routine. Next, to answer the second question, we give both theoretical explanations and empirical evidence regarding the source of the instability, demonstrating that the instability mainly comes from high inter-example correlations within analysis sets. Finally, for the third question, we discuss an initial attempt to mitigate the instability and suggest guidelines for future work such as reporting the decomposed variance for more interpretable results and fair comparison across models. Our code is publicly available at: https://github.com/owenzx/InstabilityAnalysis