Listen to this Post
2025-01-02
This article presents a comprehensive comparison of several cutting-edge Large Language Models (LLMs), including DeepSeek-V3, QVQ-72B-Preview, Falcon3 10B, Llama 3.3 70B, and Nemotron 70B. These models were rigorously evaluated using an updated version of the MMLU-Pro CS benchmark, focusing on their performance in computer science tasks.
Key Findings:
DeepSeek-V3, despite its impressive size and Mixture-of-Experts architecture, did not achieve the top spot in the MMLU-Pro CS benchmark, scoring similarly to smaller models like Qwen2.5 72B and QwQ 32B.
Llama 3.3 70B Instruct, while focused on multilinguality, demonstrated competitive performance, even in its quantized 4-bit version.
Llama 3.1 Nemotron 70B Instruct, despite its age, delivered solid results, although its conversational style may not be suitable for all applications.
QVQ-72B-Preview, designed for visual reasoning, surprisingly did not outperform smaller QwQ models in this general-purpose benchmark.
Falcon3 10B Instruct exceeded expectations, surpassing larger models like Mistral Small in performance, making it a strong contender for smaller-scale applications.
Methodology:
The MMLU-Pro benchmark assesses LLMs across various disciplines, including computer science, mathematics, and physics. This study focuses on the Computer Science category, encompassing 410 multiple-choice questions with ten options per question, significantly increasing the difficulty compared to previous versions.
Multiple test runs were conducted for each model to ensure robust and reliable results, capturing performance variability and providing insights beyond single-run scores.
What Undercode Says:
This comprehensive analysis provides valuable insights into the strengths and weaknesses of these cutting-edge LLMs.
DeepSeek-V3’s performance raises questions about the relationship between model size and performance, suggesting that factors beyond sheer scale, such as architecture and training data, play a crucial role. Further investigation is needed to understand the limitations observed in this benchmark.
Llama
QVQ-72B-Preview’s performance underscores the importance of specialized training for specific tasks. While it excels in visual reasoning, its general-purpose performance may not be as strong as anticipated.
Falcon3
This research provides a valuable resource for researchers and developers seeking to understand the capabilities and limitations of different LLMs. The findings can inform model selection, application development, and future research directions in the field of large language models.
Disclaimer:
This analysis provides a snapshot of the current state of LLM performance. The field is rapidly evolving, and new models and techniques are constantly being developed. The results presented here may not be representative of future performance.
Note: This analysis has been rewritten for clarity and conciseness, incorporating a more engaging and a dedicated section for analysis (“What Undercode Says”). The original formatting and some minor details may have been adjusted for better readability.
References:
Reported By: Huggingface.co
https://www.quora.com/topic/Technology
Wikipedia: https://www.wikipedia.org
Undercode AI: https://ai.undercodetesting.com
Image Source:
OpenAI: https://craiyon.com
Undercode AI DI v2: https://ai.undercode.help
