Statistically Profiling Biases in Natural Language Reasoning Datasets and Models
Submission Type: Regular Long Paper
Submission Track: Resources and Evaluation
Submission Track 2: Commonsense Reasoning
Keywords: nature language processing, model robustness, bias analysis
Abstract: Recent studies have shown that many natural language understanding
and reasoning datasets contain statistical cues that can be exploited by NLP models,
resulting in an overestimation of their capabilities. Existing methods, such
as “hypothesis-only” tests and CheckList, are limited in identifying these cues
and evaluating model weaknesses. We introduce ICQ (I-See-Cue), a lightweight,
general statistical profiling framework that automatically identifies potential biases
in multiple-choice NLU datasets without requiring additional test cases. ICQ
assesses the extent to which models exploit these biases through black-box testing,
addressing the limitations of current methods. In this work, we conduct a
comprehensive evaluation of statistical biases in 10 popular NLU datasets and 4
models, confirming prior findings, revealing new insights, and offering an online
demonstration system to encourage users to assess their own datasets and models.
Furthermore, we present a case study on investigating ChatGPT’s bias, providing
valuable recommendations for practical applications.
Submission Number: 2934
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