arXiv:2607.00601v1 Announce Type: new Abstract: The game of Taboo requires describing a target word without using a set of forbidden words, so that other players can guess it. This deceptively simple task combines strict lexical constraints with the need for communicatively effective descriptions, making it a compelling playground for examining how LLMs navigate competing demands at inference time. We evaluate two open-weight models under conditions that intervene at progressively deeper levels of the generative process, from prompting to generation-time constraints to internal representations
The rapid advancement of large language models necessitates continuous exploration of their capabilities and limitations in complex linguistic tasks.
Understanding how LLMs navigate constraints and communication challenges is crucial for developing more robust, reliable, and ethically aligned AI systems.
This research provides deeper insight into the internal workings of LLMs when faced with nuanced generative constraints, moving beyond simple prompt engineering.
- · AI researchers
- · LLM developers
- · AI safety researchers
- · Developers relying solely on superficial prompting
Improved methods for controlling LLM output will emerge.
More sophisticated and context-aware AI agents could be developed.
This could lead to new paradigms for human-AI collaboration in constrained environments.
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Read at arXiv cs.CL