🤖 AI Summary
This study addresses the issue of anthropomorphic outputs in large language models (LLMs) within software development toolchains, which often lead users to misattribute intentionality and comprehension capabilities, thereby undermining verification behaviors and disrupting trust calibration. The authors propose the first systematic, deployable set of output-side linguistic rules—comprising seven principles—implemented via a configuration-based system prompt that constrains known anthropomorphizing mechanisms without requiring any model modifications. Evaluated using the AnthroScore metric across 780 dialogue rounds, this approach significantly reduces anthropomorphic expressions (97% fewer anthropomorphic markers; AnthroScore reduction of 1.94 versus 0.96 in controls, p<0.001) while simultaneously shortening output length by 49%, effectively clarifying the machine’s non-human identity.
📝 Abstract
Large language models (LLMs) in research and development toolchains produce output that triggers attribution of agency and understanding -- a cognitive illusion that degrades verification behavior and trust calibration. No existing mitigation provides a systematic, deployable constraint set for output register. This paper proposes seven output-side rules, each targeting a documented linguistic mechanism, and validates them empirically. In 780 two-turn conversations (constrained vs. default register, 30 tasks, 13 replicates, 1560 API calls), anthropomorphic markers dropped from 1233 to 33 (>97% reduction, p < 0.001), outputs were 49% shorter by word count, and adapted AnthroScore confirmed the shift toward machine register (-1.94 vs. -0.96, p < 0.001). The rules are implemented as a configuration-file system prompt requiring no model modification; validation uses a single model (Claude Sonnet 4). Output quality under the constrained register was not evaluated. The mechanism is extensible to other domains.