Easy Problems That LLMs Get Wrong

TL;DR

This paper presents a comprehensive benchmark revealing significant limitations of large language models in logical reasoning, spatial understanding, and linguistic tasks, emphasizing the need for improved training and human-in-the-loop approaches.

Contribution

It introduces a new linguistic benchmark to evaluate LLMs' limitations and highlights the potential of prompt engineering and human grounding to improve model performance.

Findings

LLMs struggle with simple logical and spatial tasks

Prompt engineering can reduce some errors

Grounding models with human reasoning is essential

Abstract

We introduce a comprehensive Linguistic Benchmark designed to evaluate the limitations of Large Language Models (LLMs) in domains such as logical reasoning, spatial intelligence, and linguistic understanding, among others. Through a series of straightforward questions, it uncovers the significant limitations of well-regarded models to perform tasks that humans manage with ease. It also highlights the potential of prompt engineering to mitigate some errors and underscores the necessity for better training methodologies. Our findings stress the importance of grounding LLMs with human reasoning and common sense, emphasising the need for human-in-the-loop for enterprise applications. We hope this work paves the way for future research to enhance the usefulness and reliability of new models.