arXiv:2606.07555v1 Announce Type: cross Abstract: Glossaries, technical specifications, and system prompts routinely ask language models to use familiar words in unfamiliar ways. When this works, the lexical prior persists through override rather than being replaced: it continues to operate after the local rule applies, with the rule lowering its logit rather than installing the new meaning on top. We test this with a Stroop-style paradigm: a remapping rule ("doctor" means "forest") pitted against the query word's lexical-prior distractor ("hospital"), with matched neutral controls. Across 11
The proliferation of advanced language models necessitates a deeper understanding of how they process and potentially override learned associations, becoming critical as instructions and prompts become more complex.
This research provides crucial insights into the fundamental mechanisms of lexical processing in large language models, impacting model reliability, controllability, and the development of more sophisticated AI applications.
Our understanding of how language models 'learn' new rules versus suppressing old ones is refined, suggesting a persistence of prior knowledge even under explicit instruction.
- · AI researchers
- · Developers of custom LLMs
- · Users relying on precise model control
- · Naively prompted LLMs
- · Applications with weak instruction parsing
Improved methods for training and prompting LLMs to better handle conflicting instructions and context will emerge.
This could lead to more robust and less 'hallucinatory' AI agents capable of nuanced contextual understanding.
The enhanced controllability of AI agents might accelerate their deployment in sensitive or mission-critical applications where reliable rule-following is paramount.
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Read at arXiv cs.LG