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Easy Problems That LLMs Get Wrong
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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.
Forward citations
Cited by 3 Pith papers
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Investigating Intersectional Bias in Large Language Models using Confidence Disparities in Coreference Resolution
The abstract claims LLMs show up to 40% coreference confidence disparities across intersectional identities, but the article body is an unrelated paper on robotic fruit handling.
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