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This arXiv study measures how consistently consumer chatbots score the policy effects of US presidential actions and hypothetical reforms over a month of repeated queries. It finds that sign and ranking agreement are generally stable, but that silent service changes shift judgments, which matters for anyone treating LLM output as a reliable political or policy signal. The released dataset of 16,500 judgments gives evaluators a concrete baseline for auditing drift in deployed models.
Robustness of Chatbot LLM Judgments of Presidential Actions and Hypothetical Reforms
arXiv:2610.39398v1 Announce Type: new Abstract: Chatbots are increasingly consulted about public affairs, and their judgments of policies are useful only if they are robust. We measure this robustness in the setting of an ordinary user, through consumer chat interfaces. For 30 days, five chatbot configurations (Mistral, Gemini Flash, Claude Sonnet, and Claude Opus at two effort settings) were asked twice a day to score the long-term effect on the United States of 40 actions of the last four presidential administrations and of 15 hypothetical reforms, each with a confidence and a short comment. The judgments are robust: all configurations agree on the sign and the ranking of the administrations' mean scores, and two different configurations asked on the same day give opposite signs to the same action in about 4% of cases. Responses from different days differ more than same-day responses, but the difference grows slowly over the month, except where the service itself changed. We identify three such changes, which shifted the judgments while leaving little or no trace in the interface. Treating the comments as a retrieval task, we find them specific to each item and consistent across runs and chatbots, with lexical and embedding similarities agreeing. We release the dataset of 16,500 judgments.
Abstract:Chatbots are increasingly consulted about public affairs, and their judgments of policies are useful only if they are robust. We measure this robustness in the setting of an ordinary user, through consumer chat interfaces. For 30 days, five chatbot configurations (Mistral, Gemini Flash, Claude Sonnet, and Claude Opus at two effort settings) were asked twice a day to score the long-term effect on the United States of 40 actions of the last four presidential administrations and of 15 hypothetical reforms, each with a confidence and a short comment. The judgments are robust: all configurations agree on the sign and the ranking of the administrations' mean scores, and two different configurations asked on the same day give opposite signs to the same action in about 4% of cases. Responses from different days differ more than same-day responses, but the difference grows slowly over the month, except where the service itself changed. We identify three such changes, which shifted the judgments while leaving little or no trace in the interface. Treating the comments as a retrieval task, we find them specific to each item and consistent across runs and chatbots, with lexical and embedding similarities agreeing. We release the dataset of 16,500 judgments.
Computation and Language (cs.CL); Computers and Society (cs.CY)
arXiv:2610.39398 [cs.CL]
(or arXiv:2610.39398v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.39398 Focus to learn more arXiv-issued DOI via DataCite
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