Large Language Models are biased to overestimate profoundness
Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 9653–9661
Abstract
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 fewshot learning prompts, as opposed to chain-ofthought 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.
Authors 5
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Affiliation as printed
Centro Nacional de Inteligencia Artificial (CENIA) , Chile
Centro Nacional de Inteligencia Artificial (CENIA), Chile
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Pontificia Universidad Católica de Chile
Affiliation as printed
Centro Nacional de Inteligencia Artificial (CENIA) , Chile
Pontificia Universidad Católica de Chile , Chile
Centro Nacional de Inteligencia Artificial (CENIA), Chile
Pontificia Universidad Católica de Chile, Chile
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Affiliation as printed
Brown University , United States
Brown University, United States
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Marc–Lluís Vives Aachen
Affiliation as printed
Leiden University , the Netherlands
Leiden University, the Netherlands
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Affiliation as printed
Centro Nacional de Inteligencia Artificial (CENIA) , Chile
Centro Nacional de Inteligencia Artificial (CENIA), Chile
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