The Illusion of Role Separation: Hidden Shortcuts in LLM Role Learning (and How to Fix Them)
This addresses a critical issue for developers and users of multi-role LLMs by revealing hidden shortcuts in role learning and offering a mechanism-based fix, though it is incremental as it builds on existing fine-tuning and data augmentation methods.
The paper tackled the problem of ensuring large language models (LLMs) accurately distinguish between different input roles (e.g., system vs. user messages) to maintain consistent multi-role behavior, finding that fine-tuned models often rely on superficial shortcuts like task type exploitation and proximity to text beginnings, and proposed adjusting token-wise cues such as position IDs to improve role separation and reduce reliance on these proxies.
Large language models (LLMs) that integrate multiple input roles (e.g., system instructions, user queries, external tool outputs) are increasingly prevalent in practice. Ensuring that the model accurately distinguishes messages from each role -- a concept we call \emph{role separation} -- is crucial for consistent multi-role behavior. Although recent work often targets state-of-the-art prompt injection defenses, it remains unclear whether such methods truly teach LLMs to differentiate roles or merely memorize known triggers. In this paper, we examine \emph{role-separation learning}: the process of teaching LLMs to robustly distinguish system and user tokens. Through a \emph{simple, controlled experimental framework}, we find that fine-tuned models often rely on two proxies for role identification: (1) task type exploitation, and (2) proximity to begin-of-text. Although data augmentation can partially mitigate these shortcuts, it generally leads to iterative patching rather than a deeper fix. To address this, we propose reinforcing \emph{invariant signals} that mark role boundaries by adjusting token-wise cues in the model's input encoding. In particular, manipulating position IDs helps the model learn clearer distinctions and reduces reliance on superficial proxies. By focusing on this mechanism-centered perspective, our work illuminates how LLMs can more reliably maintain consistent multi-role behavior without merely memorizing known prompts or triggers.