大语言模型存在高估深刻性的偏差
Large Language Models are biased to overestimate profoundness
AI总结：
本研究评估大语言模型对陈述深刻性的判断能力，发现其与人类判断有相关性但普遍高估无意义陈述的深刻性，少样本提示可缩小差距，且RLHF会加剧该偏差。
摘要：
大语言模型（LLM）（如GPT-4）在自然语言处理领域的最新进展，被认为已接近通用人工智能。然而，LLM是否具备与人类相似的推理能力仍存争议。本研究评估了GPT-4及其他多种LLM对平凡陈述、励志陈述和伪深刻陈述的深刻性判断能力。研究发现，无论陈述类型和所用提示技术如何，LLM与人类的判断在逐句层面存在显著相关性。但LLM会系统性高估无意义陈述的深刻性，唯有Tk-instruct例外，它独特地会低估陈述的深刻性。与思维链提示不同，只有少样本学习提示能让LLM的评分更接近人类。此外，本研究揭示了基于人类反馈的强化学习（RLHF）可能引发的潜在偏差，即它会加剧高估陈述深刻性的偏差。
英文摘要：
Recent advancements in natural language processing by large language models (LLMs), such as GPT-4, have been suggested to approach Artificial General Intelligence. And yet, it is still under dispute whether LLMs possess similar reasoning abilities to humans. This study evaluates GPT-4 and various other LLMs in judging the profoundness of mundane, motivational, and pseudo-profound statements. We found a significant statement-to-statement correlation between the LLMs and humans, irrespective of the type of statements and the prompting technique used. However, LLMs systematically overestimate the profoundness of nonsensical statements, with the exception of Tk-instruct, which uniquely underestimates the profoundness of statements. Only few-shot learning prompts, as opposed to chain-of-thought prompting, draw LLMs ratings closer to humans. Furthermore, this work provides insights into the potential biases induced by Reinforcement Learning from Human Feedback (RLHF), inducing an increase in the bias to overestimate the profoundness of statements.
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