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arXiv cs.CL
arXiv cs.CL
6/30/2026
Generating in the Limit with Infinitely Many Hallucinations

Generating in the Limit with Infinitely Many Hallucinations

Short summary

Theoretical computer science research extending language identification to language generation in the limit framework. Key finding: allowing a small, controlled rate of invalid outputs can actually increase language coverage when adversaries withhold portions of the target. Reframes the problem as precision-recall tradeoff, moving toward more realistic LLM generation models.

  • •Extends formal language theory to modern language generation scenarios
  • •Shows controlled error rates can paradoxically improve language coverage
  • •Models LLM generation with realistic constraints: occasional hallucinations but controlled rates

Generated with AI, which can make mistakes.

#research-breakthrough
Read full article at arXiv cs.CL

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