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Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning

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arxiv 2305.17256 v2 pith:SQZ3RXLR submitted 2023-05-26 cs.CL cs.AIcs.LG

Ruixiang Tang , Dehan Kong , Longtao Huang , Hui Xue This is my paper
classification cs.CLcs.AIcs.LG
keywords promptsshortcutsin-contextlearningllmsmodelslanguagelarge
verification ladder T0 review T1 audit T2 compute T3 formal
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Large language models (LLMs) have recently shown great potential for in-context learning, where LLMs learn a new task simply by conditioning on a few input-label pairs (prompts). Despite their potential, our understanding of the factors influencing end-task performance and the robustness of in-context learning remains limited. This paper aims to bridge this knowledge gap by investigating the reliance of LLMs on shortcuts or spurious correlations within prompts. Through comprehensive experiments on classification and extraction tasks, we reveal that LLMs are "lazy learners" that tend to exploit shortcuts in prompts for downstream tasks. Additionally, we uncover a surprising finding that larger models are more likely to utilize shortcuts in prompts during inference. Our findings provide a new perspective on evaluating robustness in in-context learning and pose new challenges for detecting and mitigating the use of shortcuts in prompts.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Shortcut Learning in Generalist Robot Policies: The Role of Dataset Diversity and Fragmentation

    cs.RO 2025-08 conditional novelty 6.0 of 10

    Low within-subdataset diversity and large between-subdataset differences cause shortcut learning in generalist robot policies, and targeted augmentation can mitigate it.

  2. SPARSE Data, Rich Results: Few-Shot Semi-Supervised Learning via Class-Conditioned Image Translation

    cs.CV 2025-08 unverdicted novelty 5.0 of 10

    A GAN framework that translates unlabeled medical images between classes and fuses ensemble, time-averaged pseudo-labels outperforms six prior GAN semi-supervised methods on MedMNIST at 5-50 labels per class.

  3. Mitigating Spurious Correlations in LLMs via Causality-Aware Post-Training

    cs.LG 2025-06 conditional novelty 5.0 of 10

    Fine-tuning a 3B LLM on randomly symbolized reasoning questions reduces spurious-correlation failures and improves OOD accuracy on CLadder and PrOntoQA.

  4. Spurious Correlations and Beyond: Understanding and Mitigating Shortcut Learning in SDOH Extraction with Large Language Models

    cs.CL 2025-05 conditional novelty 5.0 of 10

    LLMs infer drug use from alcohol or smoking mentions in clinical notes, producing gender-skewed false positives that prompting only partially corrects.

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