Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning
Open accessAbstract
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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The shortcut learning problem has been observed in various natural language understanding tasks under the pretraining and fine-tuning paradigm, where models heavily rely on spurious correlations between input and labels in the fine-tuning data for prediction.
“The shortcut learning problem has been observed in various natural language understanding tasks under the pretraining and fine-tuning paradigm, where models heavily rely on spurious correlations between input and labels in the fine-tuning data for prediction [35,39,107,109,128,129].”
From Harnessing the power of llms in practice: a survey on chatGPT and beyond (2024) · cites this paper as [109]
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Cites
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works
Kaugeranna, R., Kaugeranna, E., & Aion, (. S. 4. 6. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 13,695 · see citing works
Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank
Socher, R., Perelygin, A., Wu, J., Chuang, J., Manning, C. D., Ng, A., & Potts, C. (2013) · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d13-1170 · Cited by 6,878 · see citing works
“Why Should I Trust You?”: Explaining the Predictions of Any Classifier
Ribeiro, M., Singh, S., & Guestrin, C. (2016) · Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations · doi:10.18653/v1/n16-3020 · Cited by 6,682 · see citing works
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361 · see citing works
Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352 · see citing works
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Ichter, B., Xia, F., Chi, E., Le, Q., & Zhou, D. (2022) · arXiv · doi:10.48550/arxiv.2201.11903 · Cited by 4,263 · see citing works
Shortcut learning in deep neural networks
Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., & Wichmann, F. A. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00257-z · Cited by 2,224 · see citing works
Language Models as Knowledge Bases?
Petroni, F., Rocktäschel, T., Riedel, S., Lewis, P., Bakhtin, A., Wu, Y., & Miller, A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1250 · Cited by 1,834 · see citing works
Adversarial Examples for Evaluating Reading Comprehension Systems
Jia, R., & Liang, P. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1215 · Cited by 1,410 · see citing works
A holistic lexicon-based approach to opinion mining
Ding, X., Liu, B., & Yu, P. S. (2008) · doi:10.1145/1341531.1341561 · Cited by 1,398 · see citing works
Adversarial Examples Are Not Bugs, They Are Features
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., & Madry, A. (2019) · arXiv · doi:10.48550/arxiv.1905.02175 · Cited by 971 · see citing works
Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference
McCoy, R. T., Pavlick, E., & Linzen, T. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1334 · Cited by 944 · see citing works
Beyond accuracy: behavioral testing of NLP models with CheckList
Ribeiro, M. T., Wu, T., Guestrin, C., & Singh, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.442 · Cited by 890 · see citing works
Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d17-1323 · Cited by 829 · cited 1× · see citing works
“Shortcut learning has been identified as a major cause of the low robustness in large language models and has become a benchmark for evaluating models’ generalization ability (Zhao et al., 2017; Agrawal et al., 2018; Tang et al., 2021).”
BackgroundThe ATIS spoken language systems pilot corpus
Hemphill, C. T., Godfrey, J. J., & Doddington, G. R. (1990) · DARPA Workshop on Speech and Natural Language (HLT) · doi:10.3115/116580.116613 · Cited by 810 · see citing works
Predicting the Type and Target of Offensive Posts in Social Media
Zampieri, M., Malmasi, S., Nakov, P., Rosenthal, S., Farra, N., & Kumar, R. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1144 · Cited by 781 · see citing works
Gender bias in coreference resolution: Evaluation and debiasing methods
Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers) · doi:10.18653/v1/n18-2003 · Cited by 684 · see citing works
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
Yang, J., Jin, H., Tang, R., Han, X., Feng, Q., Jiang, H., Yin, B., & Hu, X. (2023) · arXiv · doi:10.48550/arxiv.2304.13712 · Cited by 644 · see citing works
Self-citationHarnessing the power of llms in practice: a survey on chatGPT and beyond
Yang, J., Jin, H., Tang, R., Han, X., Feng, Q., Jiang, H., Zhong, S., Yin, B., & Hu, X. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3649506 · Cited by 644 · see citing works
Self-citationMultitask Prompted Training Enables Zero-Shot Task Generalization
Sanh, V., Webson, A., Raffel, C., Bach, S. H., Sutawika, L., Alyafeai, Z., Chaffin, A., Stiegler, A., Scao, T. L., Raja, A., et al. (2021) · arXiv · doi:10.48550/arxiv.2110.08207 · Cited by 612 · see citing works
Don't Just Assume; Look and Answer: Overcoming Priors for Visual Question Answering
Agrawal, A., Batra, D., Parikh, D., & Kembhavi, A. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00522 · Cited by 586 · see citing works
Calibrate Before Use: Improving Few-Shot Performance of Language Models
Zhao, T. Z., Wallace, E., Feng, S., Klein, D., & Singh, S. (2021) · arXiv · doi:10.48550/arxiv.2102.09690 · Cited by 513 · see citing works
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
Gao, L., Biderman, S., Black, S., Golding, L., Hoppe, T., Foster, C., Phang, J., He, H., Thite, A., Nabeshima, N., et al. (2021) · arXiv · doi:10.48550/arxiv.2101.00027 · Cited by 492 · see citing works
Learning To Retrieve Prompts for In-Context Learning
Rubin, O., Herzig, J., & Berant, J. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.191 · Cited by 374 · see citing works
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Qi, F., Chen, Y., Zhang, X., Li, M., Liu, Z., & Sun, M. (2021) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2021.emnlp-main.374 · Cited by 143 · see citing works
Reframing Instructional Prompts to GPTk’s Language
Mishra, S. R., Khashabi, D., Baral, C. R., Choi, Y., & Hajishirzi, H. (2022) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2022.findings-acl.50 · Cited by 140 · see citing works
Surface Form Competition: Why the Highest Probability Answer Isn’t Always Right
Holtzman, A., West, P., Shwartz, V., Choi, Y., & Zettlemoyer, L. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2021.emnlp-main.564 · Cited by 127 · see citing works
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language Models
Tu, L., Lalwani, G., Gella, S., & He, H. (2020) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00335 · Cited by 125 · see citing works
Shortcut Learning of Large Language Models in Natural Language Understanding
Du, M., He, F., Zou, N., Tao, D., & Hu, X. (2024) · Communications of the ACM · doi:10.1145/3596490 · Cited by 94 · see citing works
BadNL: Backdoor Attacks Against NLP ModelsPreprint
Chen, X., Salem, A., Michael Backes, A., Ma, S., & Zhang, Y. (2020) · arXiv (Cornell University) · Cited by 87 · see citing works
Meta-learning via Language Model In-context Tuning
Chen, Y., Zhong, R., Zha, S., Karypis, G., & He, H. (2022) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2022.acl-long.53 · Cited by 84 · see citing works
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Tang, R., Du, M., Li, Y., Liu, Z., Zou, N., & Hu, X. (2021) · Proceedings of the Web Conference 2021 · doi:10.1145/3442381.3449950 · Cited by 50 · see citing works
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A conversational movie search system based on conditional random fields
Liu, J., Cyphers, S., Pasupat, P., Mcgraw, I. C., & Glass, J. R. (2012) · Interspeech (USB) · doi:10.21437/interspeech.2012-563 · Cited by 38 · see citing works
Why Machine Reading Comprehension Models Learn Shortcuts?
Lai, Y., Zhang, C., Feng, Y., Huang, Q., & Zhao, D. (2021) · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 · doi:10.18653/v1/2021.findings-acl.85 · Cited by 32 · see citing works
Prompt-Augmented Linear Probing: Scaling Beyond The Limit of Few-shot In-Context Learners
Cho, H., Kim, H. J., Kim, J., Lee, S.-W., Lee, S.-G., Yoo, K. M., & Kim, T. (2022) · AAAI Conference on Artificial Intelligence · doi:10.48550/arxiv.2212.10873 · Cited by 30
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Lei, Y., Huang, R., Wang, L., & Beauchamp, N. J. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.682 · Cited by 26 · see citing works
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Chen, Y., Zhong, R., Zha, S., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07814 · Cited by 17 · see citing works
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Wang, T., Sridhar, R., Yang, D., et al. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.07736 · Cited by 17 · see citing works
Prompt-Augmented Linear Probing: Scaling beyond the Limit of Few-Shot In-Context Learners
Cho, H., Kim, H. J., Kim, J., Lee, S.-W., Lee, S., Yoo, K. M., & Kim, T. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i11.26495 · Cited by 11 · see citing works
Learning To Retrieve Prompts for In-Context LearningPreprint
Rubin, O., Herzig, J., & Berant, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2112.08633 · Cited by 11 · see citing works
Lei, Y., & Huang, R. (2022) · doi:10.18653/v1/2022.findings-emnlp.409 · Cited by 5 · see citing works
Flagged first, then by citations
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Wu, X., Saraf, P. P., Lee, G., Latif, E., Liu, N., & Zhaı, X. (2025) · Technology Knowledge and Learning · doi:10.1007/s10758-025-09836-8 · Cited by 43 · see citing works
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Qiu, R., Zhang, G. L., Drechsler, R., Schlichtmann, U., & Li, B. (2025) · Design, Automation, and Test in Europe · doi:10.23919/date64628.2025.10992873 · Cited by 21 · see citing works
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Ma, C., Zhao, L., Chen, Y., Guo, L., Zhang, T., Hu, X., Shen, D. G., Jiang, X., & Liu, T. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3310531 · Cited by 19 · see citing works
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Kang, X., Qu, L., Soon, L.-K., Trakic, A., Zhuo, T. Y., Emerton, P., & Grant, G. M. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.929 · Cited by 9 · see citing works
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Sheik, R., Reji, S. A., Sharon, A., Avisha, M., & Nirmala, S. J. (2025) · Artificial Intelligence and Law · doi:10.1007/s10506-025-09459-5 · Cited by 5 · see citing works
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- Authors
- Tang, R., Kong, D., Huang, L., & Xue, H.
- Affiliations
- Alibaba Group (China) (CN)
- Venue
- Findings of the Association for Computational Linguistics: ACL 2023
- Published
- 2023
- Pages
- 4645-4657
- OpenAlex
- W4385569978
- Cited by
- 31
- References
- 40
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 8 · 2023: 4
- FWCI
- 5.49
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: ACL (Findings of ACL) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Keywords
- in-context learning, large language models, shortcuts, spurious correlations, lazy learning, prompt learning, robustness, model size, classification, information extraction
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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