1/ Can Large Language Models (LLMs) truly reason? Or are they just sophisticated pattern matchers? In our latest preprint, we explore this key question through a large-scale study of both open-source like Llama, Phi, Gemma, and Mistral and leading closed models, including the recent OpenAI GPT-4o and o1-series.
arxiv.org/pdf/2410.05229
Work done with @i_mirzadeh, @KeivanAlizadeh2, Hooman Shahrokhi, Samy Bengio, @OncelTuzel.
#LLM #Reasoning #Mathematics #AGI #Research #Apple
2/ When OpenAI released GSM8K ~3 years ago, GPT-3 (175B) scored 35% on the GSM8K test. Today, models with ~3B parameters are surpassing 85%, and larger ones are hitting >95%. But has model 'reasoning' really improved? How much of this is genuine #logical/#symbolic reasoning? vs. #pattern_recognition, inadvertent data #contamination, or #overfitting?
3/ Introducing GSM-Symbolic—our new tool to test the limits of LLMs in mathematical reasoning. We create symbolic templates from the #GSM8K test set, enabling the generation of numerous instances and the design of controllable experiments. We generate 50 unique GSM-Symbolic sets, essentially like GSM8K examples but with different values and names. How do models handle these distinct sets?
4/ #Result 1: Current accuracies on GSM8K are not reliable! We observe LARGE performance variation: Llama 8B scores anywhere between 70% to 80%, Phi-3 scores between 75% and 90%, and so on. For most models, the average performance on GSM-Symbolic is lower than on GSM8K (indicated by the dashed line).
5/ #Result 2: The fragility of supposed LLM reasoning. LLMs remain sensitive to changes in proper names (e.g., people, foods, objects), and even more so when numbers are altered. Would a grade-school student's math test score vary by ~10% if we only changed the names?
6/ What if we adjust question difficulty? We introduce 3 new variants of GSM-Symbolic to study model behavior: removing one clause (GSM-M1), adding one clause (GSM-P1), or adding two clauses (GSM-P2).
7/ #Result 3: As questions increase in difficulty (M1 → Symbolic → P1 → P2), not only does performance drop, but variance also rises, making models increasingly unreliable.
8/ This begs the question: Do these models truly understand mathematical concepts? Introducing #GSM_NoOp! We add a single clause that seems relevant but doesn't contribute to the overall reasoning (hence "no-op"). Check out what happens next!
9/ #Result 4: A massive performance drop! All models, including o1 models, show significant declines. While it’ll be interesting to see how grade-school students perform on similar datasets, I doubt the drop would be this severe.“
10/ #Result 5: Can scaling data, models, or compute fundementaly solve this? We don't think so! #OpenAI's #o1-series is performing better but still suffers from slight performance variations. #o1_preview shows significant improvements, but...
11-/ .... but even o1-preview shows the same silly mistakes like this. Either it doesn't understand what 'now' is, or it doesn't understand what 'last year' is, or a more likely explanation is that its training data with inflation has this pattern, and it's following that again.
12/ Understanding LLMs' true reasoning capabilities is crucial for deploying them in real-world scenarios where accuracy and consistency are non-negotiable—especially in #AI_safety, #alignment, #education, #health_care, and #decision_making systems. Our findings emphasize the need for more robust and adaptable evaluation methods. Developing models that move beyond pattern recognition to true logical reasoning is the next big challenge for the #AI #community.
13/ Overall, we found no evidence of formal reasoning in language models including open-source models like #Llama, #Phi, #Gemma, and #Mistral and leading closed models, including the recent #OpenAI #GPT-4o and #o1-series. Their behavior is better explained by sophisticated pattern matching—so fragile, in fact, that changing names can alter results by ~10%! We can scale data, parameters, and compute—or use better training data for Phi-4, Llama-4, GPT-5. But we believe this will result in 'better pattern-matchers,' not necessarily 'better reasoners.
Check out the full paper to find out more: arxiv.org/pdf/2410.05229
Also stay tuned for the data release!
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