This is an automated archive made by the Lemmit Bot.
The original was posted on /r/singularity by /u/Wiskkey on 2024-10-12 13:05:59+00:00.
Apple AI researchers question OpenAI's claims about o1's reasoning capabilities.
A new study by Apple researchers, including renowned AI scientist Samy Bengio, calls into question the logical capabilities of today's large language models - even OpenAI's new "reasoning model" o1.
The team, led by Mehrdad Farajtabar, created a new evaluation tool called GSM-Symbolic. This tool builds on the GSM8K mathematical reasoning dataset and adds symbolic templates to test AI models more thoroughly.
The researchers tested open-source models such as Llama, Phi, Gemma, and Mistral, as well as proprietary models, including the latest offerings from OpenAI. The results, published on arXiv, suggest that even leading models such as OpenAI's GPT-4o and o1 don't use real logic, but merely mimic patterns.
GSM-Symbolic: Understanding the Limitations of Mathematical Reasoning in Large Language Models.
Recent advancements in Large Language Models (LLMs) have sparked interest in their formal reasoning capabilities, particularly in mathematics. The GSM8K benchmark is widely used to assess the mathematical reasoning of models on grade-school-level questions. While the performance of LLMs on GSM8K has significantly improved in recent years, it remains unclear whether their mathematical reasoning capabilities have genuinely advanced, raising questions about the reliability of the reported metrics. To address these concerns, we conduct a large-scale study on several SOTA open and closed models. To overcome the limitations of existing evaluations, we introduce GSM-Symbolic, an improved benchmark created from symbolic templates that allow for the generation of a diverse set of questions. GSM-Symbolic enables more controllable evaluations, providing key insights and more reliable metrics for measuring the reasoning capabilities of models. Our findings reveal that LLMs exhibit noticeable variance when responding to different instantiations of the same question. Specifically, the performance of all models declines when only the numerical values in the question are altered in the GSM-Symbolic benchmark. Furthermore, we investigate the fragility of mathematical reasoning in these models and show that their performance significantly deteriorates as the number of clauses in a question increases. We hypothesize that this decline is because current LLMs cannot perform genuine logical reasoning; they replicate reasoning steps from their training data. Adding a single clause that seems relevant to the question causes significant performance drops (up to 65%) across all state-of-the-art models, even though the clause doesn't contribute to the reasoning chain needed for the final answer. Overall, our work offers a more nuanced understanding of LLMs' capabilities and limitations in mathematical reasoning.
X thread about the paper from one of its authors. Alternate link #1. Alternate link #2.