arXiv:2606.24267v1 Announce Type: cross Abstract: While in-context learning is generally shown to be effective in Large Language Models (LLMs), bad contexts can cause performance degradation and mode collapse, a phenomenon we call "pigeonholing." **Unintentionally bad** contexts can happen without malicious jailbreaking intents: For example, a user asks the model to justify an incorrect math theorem or fails to correct the model's buggy code. Specifically, we investigate ``pigeonholing" in two scenarios: (1) when the user suggests a solution, and (2) when the conversation context includes the
The paper highlights a critical and emerging challenge in the widespread deployment of Large Language Models, as they become integrated into more complex user interactions and autonomous systems.
Understanding and mitigating 'pigeonholing' is crucial for developing robust, reliable, and trustworthy AI systems, impacting their commercial viability and societal acceptance.
This research shifts focus from solely malicious inputs to 'unintentionally bad' prompts as significant failure modes, requiring developers to consider new defensive and design strategies.
- · AI safety researchers
- · Guardrail development platforms
- · Rigorous testing methodologies
- · LLM developers without robust testing
- · Applications with unconstrained user input
- · Users relying on unverified LLM outputs
Increased emphasis will be placed on prompt engineering, validation, and contextual filtering for LLM deployments.
New tools and frameworks will emerge to automatically detect and correct 'pigeonholing' scenarios.
The development of more resilient and self-correcting AI architectures that can identify and escape invalid contexts may accelerate.
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Read at arXiv cs.AI