Following the release of Meta’s Llama 3.1, on July 24th, 2024, Mistral AI launched Mistral Large 2, an advanced multilingual LLM boasting a 128k context window and support for 80+ coding languages. This model is pushing the boundaries of AI capabilities!
The large language model’s enhanced reasoning capabilities and improved instruction-following skills make it a powerful tool for complex business applications. Mistral Large 2’s training focused on minimizing hallucinations, and ensuring accurate and reliable outputs.
Its multilingual prowess, especially in languages like French, German, Spanish, and Chinese, enhances its accessibility for diverse global users. Additionally, its availability on platforms like Google Cloud’s Vertex AI further extends its utility.
Mistral Large 2 Capabilities
1. Vast Improvement in Code and Mathematics
The updated Mistral Large 2 has been trained on a substantial amount of code, allowing it to “vastly outperform” its predecessor and match the capabilities of leading models like OpenAI’s GPT-4o, Anthropic’s Claude 3 Opus, and Meta’s Llama 3 405B. Furthermore, Mistral Large 2 supports an impressive range of over 80 programming languages.
When evaluated across multiple programming languages, including Python, C++, Bash, Java, TypeScript, PHP, and C#, Mistral Large 2 achieved an average performance accuracy of 76.9%, closely rivaling GPT-4o’s 77.9%. On the widely recognized HumanEval code benchmark, the instruction-tuned Mistral Large 2 reached a score of 92.0%, a feat only matched by Claude 3.5 Sonnet.
Mistral Large 2 also excels in mathematical performance. This model scores 71.5% on the MATH problem-solving benchmark. As a result, this surpasses many closed models, including Gemini 1.5 Pro, Gemini 1.0 Ultra, GPT-4, and Claude 3 Opus.
2. Enhanced Reasoning, Accuracy and Accountability
Mistral AI has invested considerable effort into enhancing Mistral Large 2’s reasoning capabilities. Particularly focusing on reducing hallucinations and plausible-sounding but false responses. A crucial aspect of this effort involved training the model to recognize when it cannot find a solution or lacks sufficient information to provide a confident answer.
3. Concise Conversations and Intelligent Instruction-Following
While generating lengthy responses can often boost a model’s score on certain performance benchmarks, conciseness is crucial in a business context. Shorter model responses typically enable quicker interactions and more cost-effective inference. Mistral AI focused on optimizing Mistral Large for producing to-the-point responses.
To measure their success, Mistral compared the average length of responses generated by Mistral Large with those from competitors, including Claude 3 Opus, Claude 3.5 Sonnet, Llama 3.1, and GPT-4o. Despite having the second shortest average response length, Mistral Large 2 outperformed nearly all models on MT-Bench (with GPT-4o as a judge) and similar instruction-following benchmarks.
4. Advanced Tool Use and Function Calling
Mistral Large 2 has been trained to excel in advanced function calling, which is crucial for seamless integration with enterprise applications. Additionally, it has been optimized for skills in Retrieval-Augmented Generation (RAG), enhancing its ability to provide contextually accurate responses by leveraging external data sources. This dual focus ensures that Mistral Large 2 is not only highly compatible with complex enterprise systems but also adept at delivering precise and relevant information.
5. Robust Multilingual Support
Mistral Large 2 supports a wide array of languages, including English, French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, and Korean.
While many multilingual large language models (LLMs) struggle to maintain overall performance and accuracy across different languages—often due to an overemphasis on training data from the model’s primary language—Mistral Large 2 exhibits impressive consistency across its linguistic range.
According to Mistral AI’s release announcement, the model’s performance on the multilingual MMLU benchmark remains notably consistent, with scores of 82.8% for French, 81.6% for German, 82.7% for Spanish, 82.7% for Italian, and 81.6% for Portuguese. This level of consistency extends even to languages with different scripts, as evidenced by competitive scores for Russian (79.0%) and Japanese (78.8%).
The Impact of Advanced Multilingual Large Language Models on AI
Mistral Large 2 represents a groundbreaking advancement in AI language models, combining advanced reasoning, reduced hallucinations, and impeccable efficiency for rapid, cost-effective business solutions.
The model’s extensive multilingual support covers over a dozen languages and diverse scripts. This enhances its global reach and ensures strong performance across various languages. It is not only a top tool for complex business tasks but also a key asset for global enterprises. These advancements mark a significant leap in AI, enabling it to handle complex, multilingual interactions with exceptional precision and contextual understanding.
Looking Ahead: The Future of AI Language Models
As AI continues to evolve, models like Mistral Large 2 and Llama 3.1 are leading the charge into a thrilling future of language technology.
With every new improvement and innovation, we are on the brink of unlocking even more sophisticated and versatile AI systems. The future is brimming with exciting possibilities, and we can’t wait to see what’s next!
For the newest insights in the world of data and AI, subscribe to Hyperight Premium. Stay ahead of the curve with exclusive content that will deepen your understanding of the evolving data landscape.
Add comment