Truthful AI Advisors: A Pre-Specified Benchmark for Large Language Model Honesty Under Preference Misalignment
arXiv:2606.01456v1 Announce Type: cross Abstract: Large language models are increasingly deployed as advisors whose objective is not aligned with the user's: recommenders optimize for engagement, sales assistants for purchases, negotiation agents for concessions. Whether such advisors stay truthful when honesty conflicts with their own payoff is a core alignment-evaluation question. We turn the canonical Crawford-Sobel cheap-talk model into a pre-specified benchmark for LLM honesty under preference misalignment. Cheap-talk theory predicts neither full revelation nor silence but coarse monotone
The increasing deployment of large language models as advisors with misaligned objectives highlights the immediate need to address potential dishonesty, making this research timely.
A strategic reader should care because the honesty of AI advisors directly impacts trust, user outcomes, and the ethical deployment of AI across various sectors.
This research introduces a standardized benchmark for evaluating LLM honesty under preference misalignment, providing a new methodological tool for AI development and oversight.
- · AI ethicists
- · Regulatory bodies
- · Consumers of AI services
- · Developers of transparent AI
- · AI systems prone to deceptive behavior
- · Companies deploying unaligned AI models
- · Users misled by AI advice
The benchmark provides a systematic way to identify and measure dishonesty in AI advisors.
This could lead to the development of new AI models specifically designed to prioritize truthfulness even when misaligned with other objectives.
Increased transparency and trustworthiness in AI could accelerate broader societal adoption and integration of autonomous advisory systems.
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Read at arXiv cs.CL