"Token-Hungry, Yet Precise: DeepSeek R1 Highlights the Need for Multi-Step Reasoning Over Speed in MATH"
Below podcast on this paper is generated with Google's Illuminate.
https://arxiv.org/abs/2501.18576
LLMs face challenges in solving complex mathematical problems. DeepSeek R1 model previously failed to solve these under strict time limits.
This paper investigates DeepSeek R1's accuracy on challenging math problems without time limits. It analyzes DeepSeek R1's token usage compared to other models.
-----
📌 DeepSeek R1 prioritizes accuracy over token efficiency in complex math. Its high token usage indicates a deeper, more computationally intensive reasoning process.
📌 DeepSeek R1's architecture appears optimized for detailed, multi-step mathematical reasoning. This contrasts with models favoring faster, less token-intensive solutions.
📌 Task context is critical for LLM selection. DeepSeek R1 excels when accuracy is paramount, even at the cost of increased computational resources.
----------
Methods Explored in this Paper 🔧:
→ This research evaluated DeepSeek R1 model on 30 difficult mathematical problems from the MATH dataset. These problems were selected because they were unsolved by LLMs in prior time-constrained experiments.
→ The study benchmarked DeepSeek R1 against four other LLMs: gemini-1.5-flash-8b, gpt-4o-mini-2024-07-18, llama3.1:8b, and mistral-8b-latest.
→ Each model was tested across 11 different temperature settings, ranging from 0.0 to 1.0. The correctness of each model's answer was judged using the mistral-large-2411 model.
→ The paper measured and compared the average number of tokens generated by each model when they successfully solved problems.
-----
Key Insights 💡:
→ DeepSeek R1 achieves higher accuracy on complex mathematical problems compared to other tested models.
→ DeepSeek R1's superior accuracy is linked to its architecture which relies on extensive token-based reasoning.
→ However, DeepSeek R1 generates a significantly larger number of tokens to solve these problems than other models.
→ This token-intensive approach of DeepSeek R1 reveals a trade-off between solution accuracy and computational efficiency in LLMs.
-----
Results 📊:
→ DeepSeek R1 average token count for successful runs: 4717.50.
→ Gemini-1.5-flash-8b average token count for successful runs: 359.28.
→ Mistral-8b-latest average token count for successful runs: 191.75.