Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models

The article discusses the limitations of Large Language Models (LLMs), which are often touted as foundation models due to their ability to transfer across various tasks and conditions. Despite high scores on standardized benchmarks, the authors demonstrate a significant failure in the models' function and reasoning capabilities when faced with a simple, common sense problem. The models not only provide incorrect solutions, but also display overconfidence in their errors and offer nonsensical explanations to justify their responses.

The authors argue that standard interventions, such as enhanced prompting or multi-step re-evaluation, are ineffective in correcting these errors. They call for a re-assessment of the claimed capabilities of current LLMs and advocate for the creation of standardized benchmarks that can detect these basic reasoning deficits. The authors believe that these deficits have remained undetected due to the limitations of current evaluation procedures and benchmarks.

Key takeaways:

Large Language Models (LLMs) are often described as foundation models that transfer strongly across various tasks and conditions, but this study shows a dramatic breakdown of function and reasoning capabilities in these models.
The models express strong overconfidence in their wrong solutions and provide often non-sensical 'reasoning'-like explanations to justify their clearly failed responses.
Standard interventions like enhanced prompting or multi step re-evaluation fail to correct the models' wrong solutions.
The author calls for an urgent re-assessment of the claimed capabilities of current generation of LLMs and the creation of standardized benchmarks for proper detection of basic reasoning deficits.

Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models

Key takeaways:

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