Oshan Ivantha

Wow, actual grown men are still doing the "I asked the LLM about itself and it said" thing. In 2025. Folks, LLMs don't know anything about how they themselves are built or deployed, unless they've been explicitly programmed with that information (which they almost never are).

In 1968 Finland banned for profit education, the few private schools that exist in Finland have to reinvest any profit they make or pay it back to parents. It has been in the Top 3 in education for the last 20 years. There should be no profit in education or healthcare.

📌 This paper investigates the dramatic breakdown of state-of-the-art LLMs' reasoning capabilities when confronted with a simple common sense problem called the "Alice In Wonderland (AIW) problem". This is despite their strong performance on standardized reasoning benchmarks. The key conclusion is that current LLMs lack basic reasoning skills, and existing benchmarks fail to properly detect these deficiencies. 'Alice in Wonderland: Simple Tasks Showing Complete Reasoning Breakdown in State-Of-the-Art Large Language Models' 📌 The AIW problem is a concise natural language task that asks: "Alice has N brothers and she also has M sisters. How many sisters does Alice's brother have?" While easily solvable by humans using common sense reasoning (the correct answer is M+1), most tested LLMs, including GPT-3.5/4, Claude, Gemini, LLaMA, Mistral, and others, show a severe collapse in performance, often providing nonsensical answers and reasoning. 📌 Notably, even when LLMs occasionally provide correct answers, they often express strong overconfidence in their wrong solutions and generate confabulations (persuasive but nonsensical explanations) to justify their incorrect responses. Standard interventions like enhanced prompting or asking models to re-evaluate their answers fail to improve performance. 📌 The authors introduce a harder variation called AIW+, which causes an even stronger performance collapse across all tested models, including GPT-4 and Claude 3 Opus, which performed relatively better on the original AIW problem. 📌 This study highlights a striking discrepancy between LLMs' high scores on standardized reasoning benchmarks (e.g., MMLU, ARC, Hellaswag) and their poor performance on the AIW problem, suggesting that current benchmarks do not adequately reflect models' true reasoning capabilities and weaknesses. 📌 The authors emphasize the need for the ML community to develop new reasoning benchmarks that can properly detect such deficits and guide the improvement of LLMs' reasoning skills. They also stress the importance of fully open and reproducible training pipelines, including dataset composition, to enable proper analysis and progress in this area.