Liar, Liar, Logical Mire: A Benchmark for Suppositional Reasoning in Large Language Models

TL;DR

This paper introduces the TruthQuest benchmark to evaluate large language models' ability to perform suppositional reasoning using knights and knaves puzzles, revealing significant challenges and reasoning errors in current models.

Contribution

The paper presents a new benchmark, TruthQuest, for assessing suppositional reasoning in large language models, highlighting their difficulties and error patterns.

Findings

Large language models struggle with knights and knaves puzzles.

Lower-performing models often fail to understand truth and lies.

Proficient models mainly struggle with logical implications of false statements.

Abstract

Knights and knaves problems represent a classic genre of logical puzzles where characters either tell the truth or lie. The objective is to logically deduce each character's identity based on their statements. The challenge arises from the truth-telling or lying behavior, which influences the logical implications of each statement. Solving these puzzles requires not only direct deductions from individual statements, but the ability to assess the truthfulness of statements by reasoning through various hypothetical scenarios. As such, knights and knaves puzzles serve as compelling examples of suppositional reasoning. In this paper, we introduce $\textit{TruthQuest}$, a benchmark for suppositional reasoning based on the principles of knights and knaves puzzles. Our benchmark presents problems of varying complexity, considering both the number of characters and the types of logical statements involved. Evaluations on $\textit{TruthQuest}$ show that large language models like Llama 3 and Mixtral-8x7B exhibit significant difficulties solving these tasks. A detailed error analysis of the models' output reveals that lower-performing models exhibit a diverse range of reasoning errors, frequently failing to grasp the concept of truth and lies. In comparison, more proficient models primarily struggle with accurately inferring the logical implications of potentially false statements.