A new research paper explores the discrepancy between how large language models (LLMs) 'speak' and what they 'know' regarding human opinions. The study found that for 37% of World Values Survey questions, the most probable output from an LLM's behavior distribution (what it actually says) differs from its knowledge distribution (what it knows about opinions). This gap is significant for evaluation, as a model might score well based on its knowledge but produce outputs that do not align with that knowledge. The researchers propose a balanced distribution that combines both knowledge and behavior to improve consistency and accuracy. AI
IMPACT Highlights a critical gap in LLM evaluation, suggesting current benchmarks may not accurately reflect model behavior in real-world applications.
RANK_REASON Research paper published on arXiv detailing findings about LLM behavior vs. knowledge distributions. [lever_c_demoted from research: ic=1 ai=1.0]
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