Summary of Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-of-the-art Large Language Models, by Marianna Nezhurina et al.
Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models
by Marianna Nezhurina, Lucia Cipolina-Kun, Mehdi Cherti, Jenia Jitsev
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper investigates the limitations of Large Language Models (LLMs) in generalization and reasoning. Despite being trained on vast amounts of data, LLMs such as GPT-4 and Claude 3 Opus struggle to solve a simple math problem formulated in concise natural language, known as the AIW problem. The models exhibit low average performance and significant performance fluctuations when presented with slight variations in the problem template. Furthermore, they tend to produce overconfident explanations for their incorrect solutions. Standard interventions like chain-of-thought prompting or multi-step re-evaluation fail to improve the models’ performance. These findings raise questions about the capabilities of current LLMs as claimed by standardized benchmarks and highlight the need for revised evaluation procedures that can detect these deficits in generalization and reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Large Language Models (LLMs) do when solving problems. They’re really good at doing things they’ve been trained to do, but it turns out they have trouble with simple math problems if the problem is explained in a way that’s easy for humans to understand. The models also tend to be very confident in their wrong answers and come up with reasons why they’re right. Even when people try to help them get the correct answer, it doesn’t work. This makes us wonder if we should rethink what these LLMs are capable of based on how well they do on standardized tests. |
Keywords
» Artificial intelligence » Claude » Generalization » Gpt » Prompting
