Replicating Human Motivated Reasoning Studies with LLMs

TL;DR

This study investigates whether large language models exhibit human-like motivated reasoning and finds they do not, highlighting differences in reasoning processes between humans and LLMs.

Contribution

It replicates four human motivated reasoning studies with LLMs and reveals that base models do not mimic human motivated reasoning behaviors.

Findings

LLMs do not align with expected human motivated reasoning.

Models share behaviors like abstaining and argument incorporation.

Findings impact research using LLMs for opinion and argument tasks.

Abstract

Motivated reasoning - the idea that individuals processing information may be motivated to either arrive at accurate beliefs or arrive at desired conclusions - has been well-explored as a human phenomenon. However, it remains unclear whether base LLMs are affected by motivational manipulations. Replicating 4 prior political motivated reasoning studies, we find that base LLM behavior does not align with expected human behavior. Furthermore, base LLM behavior across models shares some similarities, such as when selecting to abstain from question answering and incorporating provided arguments into opinions. The results suggest that base LLMs may not emulate human motivated reasoning processes. We emphasize the importance of these findings for researchers using LLMs to for certain tasks such as opinion replication and argument assessment.