When LLMs Meet Cunning Questions: A Fallacy Understanding Benchmark for Large Language Models

Anonymous

Abstract: Recently, Large Language Models (LLMs) have made remarkable evolutions in language understanding and generation. Following this, various benchmarks for measuring all kinds of capabilities of LLMs have sprung up. In this paper, we challenge the reasoning and understanding abilities of LLMs by proposing a $\textbf{F}$a$\textbf{L}$lacy $\textbf{U}$nderstanding $\textbf{B}$enchmark ($\textbf{FLUB}$) containing cunning questions that are easy for humans to understand but difficult for models to grasp. Specifically, the cunning questions that FLUB focuses on mainly consist of the tricky, humorous, and misleading questions collected from the real internet environment. And we design three tasks with increasing difficulty in the FLUB benchmark to evaluate the fallacy understanding ability of LLMs. Based on FLUB, we investigate the performance of multiple representative and advanced LLMs, reflecting our FLUB is challenging and worthy of more future study. Interesting discoveries and valuable insights are achieved in our extensive experiments and detailed analyses. We hope that our benchmark can encourage the community to improve LLMs' ability to understand fallacies.

Paper Type: long

Research Area: Resources and Evaluation

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data resources

Languages Studied: Chinese