TextClass Benchmark applies a continuous Elo and Meta-Elo rating to LLMs for social-science text classification; the first snapshot covers toxicity detection in Chinese, English, German, and Russian.
Are Large Language Models Chameleons? An Attempt to Simulate Social Surveys
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abstract
Can large language models (LLMs) simulate social surveys? To answer this question, we conducted millions of simulations in which LLMs were asked to answer subjective questions. A comparison of different LLM responses with the European Social Survey (ESS) data suggests that the effect of prompts on bias and variability is fundamental, highlighting major cultural, age, and gender biases. We further discussed statistical methods for measuring the difference between LLM answers and survey data and proposed a novel measure inspired by Jaccard similarity, as LLM-generated responses are likely to have a smaller variance. Our experiments also reveal that it is important to analyze the robustness and variability of prompts before using LLMs to simulate social surveys, as their imitation abilities are approximate at best.
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TextClass Benchmark: A Continuous Elo Rating of LLMs in Social Sciences
TextClass Benchmark applies a continuous Elo and Meta-Elo rating to LLMs for social-science text classification; the first snapshot covers toxicity detection in Chinese, English, German, and Russian.