Lost in the Prompt Order: Revealing the Limitations of Causal Attention in Language Models
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.
69706674a8be625b19c2afa1
Hyunjong Ok
69706674a8be625b19c2afa2
Jaeho Lee
Lost in the Prompt Order: Revealing the Limitations of Causal Attention in Language Models
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.
Research reveals that causal attention in language models creates information bottlenecks when question-answer options follow context, leading to performance drops of over 14 percentage points compared to reversed prompt ordering.
large language models prompt structure multiple-choice question answering causal attention causal mask information bottleneck