Easy Problems That LLMs Get Wrong
Sean Williams, James Huckle
TL;DR
The paper introduces a Linguistic Benchmark to diagnose Large Language Model limitations across logical, spatial, linguistic, and common-sense domains, revealing substantial gaps relative to human performance. It demonstrates that prompt engineering, including clarifying questions, can materially improve correctness and reasoning quality, but also highlights persistent reliability and generalization challenges. Through a cross-model evaluation on a 30-question benchmark, the work emphasizes the need for human-in-the-loop and robust grounding to ensure enterprise-ready performance. It discusses implications for benchmarking practices, model deployment, and future research directions to push toward more reliable and interpretable AI systems.
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.