Discourse-Role Labels as Presentation-Time Variables for Context Use in Language Models
Context-augmented language model systems often wrap supplied content with labels such as Reference:, Evidence:, Instruction:, Note:, or Example:, but the effect of these labels on reader-model behavior remains underexplored. We introduce a paired fixed-content probe over 500 MMLU-Pro items: each item receives the same misleading answer-bearing assertion under different discourse-role labels, and adoption is measured by whether the model outputs the injected wrong option. Across GPT-5.5, DeepSeek V4 Pro, Llama-3-8B-Instruct, and Qwen2.5-7B-Instruct, Misleading Adoption Rate shifts by 56-84 percentage points. Binding or source-like labels such as Instruction: and Reference: produce high ado...
| Authors | Jianguo Zhu, Xiangmei Li, Wenjie Liu |
|---|---|
| Primary category | cs.CL |
| Published | 2 June 2026 |
| Last updated at source | 8 June 2026 |
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