Belief in the Machine: Investigating Epistemological Blind Spots of Language Models

TL;DR

This paper systematically evaluates the epistemic reasoning capabilities of modern language models, revealing significant limitations in understanding belief, knowledge, and truth, which are critical for reliable decision-making in sensitive fields.

Contribution

It introduces a new dataset, KaBLE, and provides a comprehensive analysis of LMs' epistemic reasoning, highlighting key weaknesses and biases in current models.

Findings

LMs perform well on factual scenarios but poorly on false and belief-related tasks.

They struggle with recognizing and affirming personal beliefs, especially when contradictory to facts.

A bias exists in processing first-person versus third-person beliefs, with better performance on third-person tasks.

Abstract

As language models (LMs) become integral to fields like healthcare, law, and journalism, their ability to differentiate between fact, belief, and knowledge is essential for reliable decision-making. Failure to grasp these distinctions can lead to significant consequences in areas such as medical diagnosis, legal judgments, and dissemination of fake news. Despite this, current literature has largely focused on more complex issues such as theory of mind, overlooking more fundamental epistemic challenges. This study systematically evaluates the epistemic reasoning capabilities of modern LMs, including GPT-4, Claude-3, and Llama-3, using a new dataset, KaBLE, consisting of 13,000 questions across 13 tasks. Our results reveal key limitations. First, while LMs achieve 86% accuracy on factual scenarios, their performance drops significantly with false scenarios, particularly in belief-related tasks. Second, LMs struggle with recognizing and affirming personal beliefs, especially when those beliefs contradict factual data, which raises concerns for applications in healthcare and counseling, where engaging with a person's beliefs is critical. Third, we identify a salient bias in how LMs process first-person versus third-person beliefs, performing better on third-person tasks (80.7%) compared to first-person tasks (54.4%). Fourth, LMs lack a robust understanding of the factive nature of knowledge, namely, that knowledge inherently requires truth. Fifth, LMs rely on linguistic cues for fact-checking and sometimes bypass the deeper reasoning. These findings highlight significant concerns about current LMs' ability to reason about truth, belief, and knowledge while emphasizing the need for advancements in these areas before broad deployment in critical sectors.