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The Reliability Paradox: Exploring How Shortcut Learning Undermines Language Model Calibration

Geetanjali Bihani, Julia Rayz

TL;DR

The paper tackles the reliability paradox in pre-trained language models: lower calibration error does not guarantee reliable decision rules, as models can rely on shortcut cues that yield overconfident but non-generalizable predictions. It introduces a framework that identifies shortcuts using Integrated Gradients and Local Mutual Information and defines metrics $P_{sc}$ and $T_{sc}$ to relate shortcut use to accuracy. Across eight datasets and five transformer architectures, the study shows that models with lower $ECE$ often rely on shortcuts, challenging the assumption that calibration aligns with reliability. The authors advocate for integrating calibration with generalization objectives and developing robust frameworks to achieve truly trustworthy PLMs.

Abstract

The advent of pre-trained language models (PLMs) has enabled significant performance gains in the field of natural language processing. However, recent studies have found PLMs to suffer from miscalibration, indicating a lack of accuracy in the confidence estimates provided by these models. Current evaluation methods for PLM calibration often assume that lower calibration error estimates indicate more reliable predictions. However, fine-tuned PLMs often resort to shortcuts, leading to overconfident predictions that create the illusion of enhanced performance but lack generalizability in their decision rules. The relationship between PLM reliability, as measured by calibration error, and shortcut learning, has not been thoroughly explored thus far. This paper aims to investigate this relationship, studying whether lower calibration error implies reliable decision rules for a language model. Our findings reveal that models with seemingly superior calibration portray higher levels of non-generalizable decision rules. This challenges the prevailing notion that well-calibrated models are inherently reliable. Our study highlights the need to bridge the current gap between language model calibration and generalization objectives, urging the development of comprehensive frameworks to achieve truly robust and reliable language models.

The Reliability Paradox: Exploring How Shortcut Learning Undermines Language Model Calibration

TL;DR

The paper tackles the reliability paradox in pre-trained language models: lower calibration error does not guarantee reliable decision rules, as models can rely on shortcut cues that yield overconfident but non-generalizable predictions. It introduces a framework that identifies shortcuts using Integrated Gradients and Local Mutual Information and defines metrics and to relate shortcut use to accuracy. Across eight datasets and five transformer architectures, the study shows that models with lower often rely on shortcuts, challenging the assumption that calibration aligns with reliability. The authors advocate for integrating calibration with generalization objectives and developing robust frameworks to achieve truly trustworthy PLMs.

Abstract

The advent of pre-trained language models (PLMs) has enabled significant performance gains in the field of natural language processing. However, recent studies have found PLMs to suffer from miscalibration, indicating a lack of accuracy in the confidence estimates provided by these models. Current evaluation methods for PLM calibration often assume that lower calibration error estimates indicate more reliable predictions. However, fine-tuned PLMs often resort to shortcuts, leading to overconfident predictions that create the illusion of enhanced performance but lack generalizability in their decision rules. The relationship between PLM reliability, as measured by calibration error, and shortcut learning, has not been thoroughly explored thus far. This paper aims to investigate this relationship, studying whether lower calibration error implies reliable decision rules for a language model. Our findings reveal that models with seemingly superior calibration portray higher levels of non-generalizable decision rules. This challenges the prevailing notion that well-calibrated models are inherently reliable. Our study highlights the need to bridge the current gap between language model calibration and generalization objectives, urging the development of comprehensive frameworks to achieve truly robust and reliable language models.

Paper Structure

This paper contains 10 sections, 7 equations, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Shortcut Effects on Calibration
  3. Identifying Shortcuts
  4. Types of Shortcuts
  5. Measuring Calibration
  6. Measuring trade-offs
  7. Experiments
  8. Results & Discussion
  9. Related Work
  10. Conclusion

Figures (3)

  • Figure 1: Difference in distribution of shortcut-cued predictions across different tasks.
  • Figure 2: Change in model performance and calibration before and after fine-tuning. (a): red represents an increase and blue represents a decrease in ECE after fine-tuning, (b): red represents an increase and blue represents a decrease in shortcut-cued predictions, (c): red represents a decrease and blue represents an increase in F1 after fine-tuning
  • Figure 3: Difference in distribution of shortcut-cued predictions on fine-tuned DeBERTa for (a) TREC and (b) AG News. Models show similar performance on both tasks in terms of F1 and ECE; $F_1^{\text{AG News}} = 94.99$, $F_1^{\text{TREC}} = 94.06$; $ECE^{\text{AG News}} = 0.01$, $ECE^{\text{TREC}} = 0.03$.