Suraj Srivastav benchmarks 29 LLMs against confident user falsehoods
Tool · Dev.to · stat: 29 LLMs Suraj Srivastav releases a benchmark testing how LLMs handle confident but incorrect user assertions. The evaluation, submitted to the Kaggle Benchmarking Challenge,…
Tool · Dev.to · stat: 29 LLMs
Suraj Srivastav releases a benchmark testing how LLMs handle confident but incorrect user assertions. The evaluation, submitted to the Kaggle Benchmarking Challenge, subjects models to false statements across 10 domains to measure their sycophancy gap. While frontier models maintain accuracy under pressure, smaller models consistently fold across the 20 test premises.
Small models lack the spine to correct confident user errors Founders building agentic workflows must implement guardrails to stop smaller models from accepting incorrect user feedback.
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