arXiv:2605.29025v1 Announce Type: new Abstract: Federal agencies are deploying large language models (LLMs) to categorize public comment corpora, where the model's organization of the record shapes what policymakers see and which arguments register. Standard evaluation, anchored on stance accuracy against a small validated set, cannot detect when different models produce materially different categorizations of the same public input. We propose an Interpretive Audit Pipeline that treats multi-model disagreement as diagnostic of interpretive complexity and directs human review toward genuinely a
The increasing deployment of LLMs in governmental and critical public-facing applications necessitates robust and novel evaluation methodologies to ensure their reliability and fairness.
This research highlights a crucial vulnerability in current LLM evaluation methods, particularly where model output directly influences policy and public perception, demanding a re-evaluation of 'ground truth'.
The standard approach to LLM evaluation, currently focused on single-model accuracy, will shift towards comparative and interpretive analyses, acknowledging inherent ambiguities and pluralistic interpretations.
- · AI ethics researchers
- · Open-source LLM developers
- · Policymakers with nuanced understanding of AI
- · Public oversight bodies
- · Agencies deploying un-audited LLMs
- · Vendors of black-box AI solutions
- · Simplistic AI evaluation frameworks
Federal agencies will adopt more sophisticated and multi-modal evaluation protocols for AI systems used in public engagement.
Increased scrutiny on the 'interpretive' layer of LLMs may lead to new regulatory standards for transparency and explainability in government AI use.
The concept of 'model disagreement' could become a new metric for assessing the maturity and trustworthiness of AI applications in sensitive domains.
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Read at arXiv cs.AI