arXiv:2606.05025v1 Announce Type: new Abstract: Large language models (LLMs) suffer from shortcut learning: they systematically fail on out-of-distribution (OOD) inputs whose semantic surface differs from training data, even when the logical structure is identical. This undermines knowledge distillation pipelines that transfer chain-of-thought reasoning to smaller students. We introduce Invariant Gradient Alignment (IGA), a training framework that aligns gradient updates across semantically diverse but logically isomorphic examples via three innovations: (i) Logical Isomer Sets, groups of prob
The proliferation of advanced LLMs highlights their present limitations in robust, out-of-distribution reasoning, prompting active research into solutions.
Improving LLM robustness against out-of-distribution inputs is crucial for their reliable deployment in critical applications and for achieving more generalizable AI.
This research introduces a novel training framework designed to mitigate shortcut learning in LLMs, potentially leading to more reliable and adaptable AI models.
- · AI developers
- · Enterprises deploying LLMs
- · AI safety researchers
- · Companies relying on brittle LLM applications
- · Developers ignoring OOD robustness
Increased reliability and broader applicability of large language models.
Reduced need for extensive re-training or fine-tuning for new, slightly varied datasets, accelerating AI deployment cycles.
Enhanced trust in AI systems could accelerate adoption in highly sensitive sectors, influencing regulatory frameworks for AI robustness standards.
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Read at arXiv cs.LG