NishMath

Multiple discussions revolve around the concept of AI agents, exploring their architecture, functionalities, and applications in various domains. The focus is on building robust agent frameworks, enabling dynamic workflow management, and optimizing performance.The use of AI agents in different tasks indicates a growing trend in the application of AI for complex problem-solving and automation.

DeepSeek has released its v3 model which was trained on 14.8 trillion tokens, with post-training including supervised fine tuning and reinforcement learning. This has resulted in a model that benchmarks comparably to Claude 3.5 Sonnet but trained at much lower costs. DeepSeek v3 was trained with 2,788,000 H800 GPU hours at an estimated cost of $5.576 million where as Meta AI’s Llama 3.1 405B (smaller than DeepSeek v3 at 685B parameters) was trained with 30,840,000 GPU hours on 15 trillion tokens. Deepseek also introduced API access with competitive pricing.

This cluster is about practical mathematical applications and explanations, aimed at different skill levels. Topics include linear regression from theory to implementation, the math behind the Cat in the Hat, and how LLMs can solve math problems by coding. It also discusses multinomial distributions, uniform and normal distributions, and how to use ChatGPT to ace math exams. These articles demonstrate various ways mathematical concepts are taught and used in real-world scenarios.

A research paper titled “GSM-Symbolic” by Mirzadeh et al. sheds light on the limitations of Large Language Models (LLMs) in mathematical reasoning. The paper introduces a new benchmark, GSM-Symbolic, designed to test LLMs’ performance on various mathematical tasks. The analysis revealed significant variability in model performance across different instantiations of the same question, raising concerns about the reliability of current evaluation metrics. The study also demonstrated LLMs’ sensitivity to changes in numerical values, suggesting that their understanding of mathematical concepts might not be as robust as previously thought. The authors introduce GSM-NoOp, a dataset designed to further challenge LLMs’ reasoning abilities by adding seemingly relevant but ultimately inconsequential information. This led to substantial performance drops, indicating that current LLMs might rely more on pattern matching than true logical reasoning. The research highlights the need for addressing data contamination issues during LLM training and utilizing synthetic datasets to improve models’ mathematical reasoning capabilities.