arXiv:2606.04109v1 Announce Type: new 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-Instruc
The proliferation of advanced language models necessitates a deeper understanding of how their internal 'reasoning' and output generation are influenced by subtle input variations, especially as these models move towards more autonomous roles.
Understanding how discourse-role labels impact model behavior is critical for developing more robust, reliable, and steerable AI systems, directly affecting their safety and effectiveness in real-world applications.
This research provides a methodology and initial findings on how specific input labels influence the adoption of injected (potentially misleading) information by state-of-the-art language models, indicating a lever for influence or vulnerability.
- · AI safety researchers
- · Developers of custom large language models
- · Sectors relying on AI for critical decision-making
- · Developers of un-robust prompt engineering techniques
- · Users unaware of prompt vulnerabilities
- · Systems susceptible to adversarial prompting
This research reveals a specific vulnerability in how current LLMs process and are influenced by contextual cues.
It will drive the development of new prompt engineering strategies and model architectures designed to be more resilient to these types of manipulative inputs.
Improved understanding and control over LLM 'reasoning' will accelerate their deployment in high-stakes autonomous agentic systems.
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