I just read an intriguing research article titled “Alice in Wonderland: Simple tasks showing complete reasoning breakdown in state-of-the-art large language models,” available at https://lnkd.in/dpNrmQFB.
The paper discusses the challenges faced by large language models (LLMs) in handling basic reasoning tasks. It specifically evaluates their performance on a straightforward problem known as the “Alice in Wonderland problem” (AIW), which asks: “Alice has 4 brothers, and she also has 1 sister. How many sisters does Alice’s brother have?”
The findings reveal that they struggle significantly with simple reasoning questions, often providing incorrect and overly confident answers, accompanied by illogical explanations. The best correct response rate is about 60% for ChatGPT-4 but falls to 5% for most models.
For a more complex problem (AIW+): “Alice has 3 sisters. Her mother has 1 sister who does not have children – she has 7 nephews and nieces and also 2 brothers. Alice’s father has a brother who has 5 nephews and nieces in total, and who also has 1 son. How many cousins does Alice’s sister have?” The best correct response rate is about 4% for the best models.
The study emphasizes their persistent errors even when faced with interventions such as enhanced prompting or re-evaluation.
I wondered: what is ChatGPT’s opinion about this evaluation?
The answer is interesting: “This discrepancy likely stems from how these models learn: by detecting patterns in vast amounts of data rather than understanding underlying principles or logic as humans do. This means their responses can appear convincing yet be fundamentally flawed, especially in novel situations or simple reasoning puzzles that require a clear understanding of context and relationships rather than pattern matching.”
Indeed, it seems that ChatGPT is correct: LLMs excel at identifying patterns, yet they fall short when it comes to true reasoning. As highlighted in my earlier article, while LLMs are powerful tools, it’s essential to recognize their limitations to utilize them effectively.
English 
French