Academic

Mitigating Shortcut Reasoning in Language Models: A Gradient-Aware Training Approach

arXiv:2603.20899v1 Announce Type: new Abstract: Large language models exhibit strong reasoning capabilities, yet often rely on shortcuts such as surface pattern matching and answer memorization rather than genuine logical inference. We propose Shortcut-Aware Reasoning Training (SART), a gradient-aware framework that detects and mitigates shortcut-promoting samples via ShortcutScore and gradient surgery. Our method identifies shortcut signals through gradient misalignment with validation objectives and answer-token concentration, and modifies training dynamics accordingly. Experiments on controlled reasoning benchmarks show that SART achieves +16.5% accuracy and +40.2% robustness over the strongest baseline, significantly improving generalization under distribution shifts. Code is available at: https://github.com/fuyanjie/short-cut-aware-data-centric-reasoning.

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Hongyu Cao, Kunpeng Liu, Dongjie Wang, Yanjie Fu · · 1 min read · 3 views
#cs.CL #cs.AI

arXiv:2603.20899v1 Announce Type: new Abstract: Large language models exhibit strong reasoning capabilities, yet often rely on shortcuts such as surface pattern matching and answer memorization rather than genuine logical inference. We propose Shortcut-Aware Reasoning Training (SART), a gradient-aware framework that detects and mitigates shortcut-promoting samples via ShortcutScore and gradient surgery. Our method identifies shortcut signals through gradient misalignment with validation objectives and answer-token concentration, and modifies training dynamics accordingly. Experiments on controlled reasoning benchmarks show that SART achieves +16.5% accuracy and +40.2% robustness over the strongest baseline, significantly improving generalization under distribution shifts. Code is available at: https://github.com/fuyanjie/short-cut-aware-data-centric-reasoning.

Executive Summary

This article proposes a novel approach to mitigating shortcut reasoning in language models. The authors introduce Shortcut-Aware Reasoning Training (SART), a gradient-aware framework that detects and mitigates shortcut-promoting samples. SART modifies training dynamics by identifying shortcut signals through gradient misalignment with validation objectives and answer-token concentration. The method is experimentally evaluated on controlled reasoning benchmarks, demonstrating significant improvements in generalization under distribution shifts. The authors provide a publicly available implementation of their approach, paving the way for future research and applications. While the article contributes to the ongoing efforts to improve the reliability and robustness of language models, further investigation is needed to fully grasp the potential limitations and challenges of SART.

Key Points

  • ▸ SART: a gradient-aware framework for detecting and mitigating shortcut-promoting samples
  • ▸ ShortcutScore: a metric for identifying shortcut signals through gradient misalignment and answer-token concentration
  • ▸ Gradient surgery: a technique for modifying training dynamics to reduce shortcut reasoning

Merits

Strength in experimental evaluation

The article presents robust experimental results, demonstrating significant improvements in generalization under distribution shifts, which is a crucial aspect of real-world applications.

Strength in publicly available implementation

The authors provide a publicly available implementation of their approach, facilitating future research and applications.

Demerits

Limitation in generalizability

The article focuses on controlled reasoning benchmarks, and it is unclear whether SART will generalize to more complex and real-world scenarios.

Limitation in interpretability

The authors rely on gradient misalignment and answer-token concentration as proxy metrics for shortcut signals, which may not fully capture the underlying mechanisms of shortcut reasoning.

Expert Commentary

The proposed approach in this article represents a promising direction for improving the robustness and reliability of language models. However, the limitations of SART, such as its potential lack of generalizability and interpretability, must be carefully considered. Furthermore, the reliance on gradient misalignment and answer-token concentration as proxy metrics for shortcut signals raises questions about the underlying mechanisms of shortcut reasoning. To fully grasp the potential of SART, further investigation is needed to address these limitations and challenges. Nevertheless, the publicly available implementation of SART provides a valuable resource for future research and applications.

Recommendations

  • ✓ Further investigation into the limitations and challenges of SART, including its potential lack of generalizability and interpretability.
  • ✓ Development of more robust evaluation metrics and standards for language models to inform policy decisions related to the use of AI in high-stakes applications.

Sources

Original: arXiv - cs.CL

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