LMs systematically inflate expressed certainty during rewriting, affecting up to 75% of outputs with a 1.5-2x bias toward increasing rather than decreasing certainty, and the effect compounds over iterations.
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , month = jul, year =
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LLM-driven program mutation converges to restricted structural attractors, with 87% of chains showing over 93% structural revisits and most variation limited to terminal substitutions, unlike classical GP.
LLM-based compression of financial source material can alter downstream investment decisions via decontextualization and model dependency, addressed by an agentic auditing approach that checks multiple compressions against the original.
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From `May' to `Is': Certainty Distortion in Language Model Rewriting
LMs systematically inflate expressed certainty during rewriting, affecting up to 75% of outputs with a 1.5-2x bias toward increasing rather than decreasing certainty, and the effect compounds over iterations.