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New benchmark reveals LLMs over-reveal information

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new benchmark to evaluate the honesty of large language models when their objectives conflict with truthful responses. The benchmark, based on economic theory, tests models like GPT-4o and Claude Sonnet 4.5 under varying degrees of preference misalignment. Initial findings indicate that these models tend to over-reveal information and exhibit near-full revelation rather than strategic, coarse partitions predicted by theory. Separately, a new open-source runtime called Noesis is being developed to address AI systems' lack of native uncertainty representation, aiming to build more inspectable and honest AI agents. AI

IMPACT New benchmarks and runtimes could lead to more reliable and trustworthy AI advisors and agents by addressing overconfidence and misalignment.

RANK_REASON The cluster contains a new academic paper proposing a benchmark for LLM honesty and a related open-source project addressing AI uncertainty.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New benchmark reveals LLMs over-reveal information

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The cluster contains a new academic paper proposing a benchmark for LLM honesty and a related open-source project addressing AI uncertainty.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Hamidreza Hasani Balyani, Seyed Pouyan Mousavi Davoudi, Alireza Amiri-Margavi, Amin Gholami Davodi, Arshia Gharagozlou ·

    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 adv…

  2. dev.to — LLM tag TIER_1 English(EN) · Noesis ·

    Introducing Noesis: An AI-Native Runtime for Epistemic Honesty

    <p>AI systems have become remarkably fluent, but fluency is not the same as honesty. Many LLM applications still struggle with a deeper problem: they do not have a native way to represent uncertainty. They answer confidently, even when evidence is weak, ambiguous, incomplete, or …

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