Discourse-Role Labels as Presentation-Time Variables for Context Use in Language Models
A new study investigates how discourse-role labels such as 'Reference:', 'Instruction:', and 'Example:' affect language models' reliance on context. Using a paired fixed-content probe on 500 MMLU-Pro items, the researchers found that labels can shift misleading adoption rates by 56-84 percentage points. Labels like Instruction and Reference increase adoption, while Example suppresses it. The study recommends that RAG benchmarks report and control wrapper labels.
[2606.04109] Discourse-Role Labels as Presentation-Time Variables for Context Use in Language Models
[Submitted on 2 Jun 2026]
Title:Discourse-Role Labels as Presentation-Time Variables for Context Use in Language Models
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Abstract: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 adoption, whereas Example: consistently suppresses it. Paired tests, bootstrap intervals, final-instruction ablations, and Qwen final-step log-probability probes support a label-conditioned candidate preference. Boundary probes show where the effect weakens or persists: arithmetic tasks reduce adoption, passage-shaped external context preserves smaller label gaps, short-answer evaluation rules out option-letter copying, and nested-label conflicts suggest that illustrative framing can delimit adoption scope. A 200-case single-author manual audit confirms that the short-answer contrasts are stable under conservative adjudication. The resulting claim is bounded but practical: context-utilization and reader-side RAG benchmarks should report and control wrapper labels, because presentation choices can change measured reliance on supplied context.
Comments: Preprint. 1 figure, 9 tables
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2606.04109 [cs.CL]
(or arXiv:2606.04109v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.04109
arXiv-issued DOI via DataCite (pending registration)
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From: JianGuo Zhu [view email] [v1] Tue, 2 Jun 2026 18:12:57 UTC (55 KB)
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