Lost in the Prompt Order: Revealing the Limitations of Causal Attention in Language Models

Hyunjong Ok, Jaeho Lee


Abstract
Large language models exhibit surprising sensitivity to the structure of the prompt, but the mechanisms underlying this sensitivity remain poorly understood. In this work, we conduct an in-depth investigation on a striking case: in multiple-choice question answering, placing context before the questions and options (CQO) outperforms the reverse order (QOC) by over 14%p, consistently over a wide range of models and datasets. Through systematic architectural analysis, we identify causal attention as the core mechanism: in QOC prompts, the causal mask prevents option tokens from attending to context, creating an information bottleneck where context becomes invisible to options.
Anthology ID:
2026.findings-acl.1921
Volume:
Findings of the Association for Computational Linguistics: ACL 2026
Month:
July
Year:
2026
Address:
San Diego, California, United States
Editors:
Maria Liakata, Viviane P. Moreira, Jiajun Zhang, David Jurgens
Venue:
Findings
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Publisher:
Association for Computational Linguistics
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Pages:
38566–38587
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URL:
https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1921/
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Cite (ACL):
Hyunjong Ok and Jaeho Lee. 2026. Lost in the Prompt Order: Revealing the Limitations of Causal Attention in Language Models. In Findings of the Association for Computational Linguistics: ACL 2026, pages 38566–38587, San Diego, California, United States. Association for Computational Linguistics.
Cite (Informal):
Lost in the Prompt Order: Revealing the Limitations of Causal Attention in Language Models (Ok & Lee, Findings 2026)
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https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1921.pdf
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