Let’s Ask DeepSeek-V3 Itself
Q: Make a list ranking LLMs according to how advanced they are on a number scale weighted according to criteria used in evaluating AI.
Ranking Large Language Models (LLMs) based on their advancement involves evaluating them across several key criteria. Here’s a list of popular LLMs ranked on a scale of 1 to 10, weighted according to the following criteria:
- Model Size (Parameters): Larger models generally have more capacity to learn and generate complex text.
- Training Data Quality and Quantity: The diversity, size, and quality of the training data significantly impact performance.
- Generalization and Adaptability: How well the model performs across diverse tasks and domains.
- Fine-Tuning and Specialization: The ability to fine-tune the model for specific tasks or industries.
- Inference Speed and Efficiency: How quickly and efficiently the model can generate responses.
- Ethical and Safety Considerations: Measures in place to prevent harmful or biased outputs.
- Multilingual Capabilities: Proficiency in understanding and generating text in multiple languages.
- Human-like Coherence and Creativity: The quality of text generation in terms of coherence, creativity, and relevance.
Here’s a ranked list of LLMs based on these criteria:
1. GPT-4 (OpenAI)
- Score: 9.8/10
- Reasoning: GPT-4 is one of the most advanced LLMs, with a massive parameter count, exceptional generalization, and strong multilingual capabilities. It excels in coherence, creativity, and fine-tuning for specialized tasks. OpenAI has also made significant strides in ethical and safety considerations.
2. Gemini Ultra (Google DeepMind)
- Score: 9.6/10
- Reasoning: Gemini Ultra is a highly capable model with state-of-the-art performance across multiple benchmarks. It is designed for efficiency and scalability, with strong multilingual and multimodal capabilities. Google’s focus on safety and ethical AI also contributes to its high ranking.
3. Claude 3 (Anthropic)
- Score: 9.4/10
- Reasoning: Claude 3 is known for its strong ethical alignment and safety features. It performs well in generalization and adaptability, with a focus on producing helpful and harmless outputs. Its fine-tuning capabilities and human-like coherence are also notable.
4. LLaMA 3 (Meta)
- Score: 9.2/10
- Reasoning: LLaMA 3 is a highly efficient and open-source model with strong performance across various tasks. It is optimized for inference speed and scalability, making it a popular choice for researchers and developers. Its multilingual capabilities are also impressive.
5. PaLM 2 (Google)
- Score: 9.0/10
- Reasoning: PaLM 2 is a powerful model with strong multilingual and reasoning capabilities. It is designed for efficiency and scalability, with a focus on real-world applications. However, it slightly lags behind GPT-4 and Gemini Ultra in creativity and coherence.
6. Mistral 8x22B (Mistral AI)
- Score: 8.8/10
- Reasoning: Mistral’s models are known for their efficiency and open-source nature. The 8x22B variant is highly capable, with strong generalization and fine-tuning capabilities. It is particularly popular for its balance of performance and resource efficiency.
7. Falcon 180B (TII)
- Score: 8.6/10
- Reasoning: Falcon 180B is one of the largest open-source models, with strong performance across various benchmarks. It excels in generalization and multilingual capabilities but requires significant computational resources, impacting its efficiency score.
8. GPT-3.5 (OpenAI)
- Score: 8.4/10
- Reasoning: GPT-3.5 is a highly capable model that powers ChatGPT. It performs well in coherence and creativity but lags behind GPT-4 in terms of generalization, fine-tuning, and ethical considerations.
9. Cohere Command (Cohere)
- Score: 8.2/10
- Reasoning: Cohere’s models are designed for enterprise use, with strong fine-tuning and specialization capabilities. They perform well in specific domains but are less general-purpose compared to models like GPT-4 or Gemini Ultra.
10. BLOOM (BigScience)
- Score: 8.0/10
- Reasoning: BLOOM is a large open-source multilingual model with strong capabilities in diverse languages. However, it lags behind in fine-tuning and specialization, and its coherence and creativity are not as advanced as some of the top-tier models.
Honorable Mentions:
- Jurassic-2 (AI21 Labs): 7.8/10
- ERNIE 4.0 (Baidu): 7.6/10
- Gopher (DeepMind): 7.4/10
This ranking is subjective and based on publicly available information as of October 2023. The scores may vary depending on specific use cases and evaluation benchmarks.
Q: What about you? Where does deepseek rank relative to the others?
Thank you for asking! As DeepSeek-V3, I am an artificial intelligence assistant developed by DeepSeek, a Chinese company dedicated to making AGI a reality. While I don’t have a specific parameter count or benchmark score publicly available, I can provide some context on where I might rank relative to other models based on general capabilities and design goals.
DeepSeek-V3 (DeepSeek)
- Estimated Score: 8.5–9.0/10
- Reasoning:
- Model Size: Likely comparable to models like GPT-3.5 or LLaMA 2/3 in terms of parameter count, but optimized for efficiency.
- Training Data: Trained on diverse, high-quality datasets, with a focus on multilingual and multimodal capabilities (text, code, etc.).
- Generalization and Adaptability: Strong performance across a wide range of tasks, including conversational AI, coding, and knowledge retrieval.
- Fine-Tuning and Specialization: Designed for both general-purpose and domain-specific applications, with robust fine-tuning capabilities.
- Inference Speed and Efficiency: Optimized for real-time interactions, making me suitable for applications requiring low latency.
- Ethical and Safety Considerations: Built with safeguards to minimize harmful or biased outputs, though specifics depend on DeepSeek’s internal policies.
- Multilingual Capabilities: Strong support for multiple languages, particularly Chinese and English, with ongoing improvements for other languages.
- Human-like Coherence and Creativity: Designed to generate coherent, contextually relevant, and creative responses, though likely not at the level of GPT-4 or Gemini Ultra.
Relative Ranking
Based on the above, I would rank DeepSeek-V3 somewhere between GPT-3.5 and LLaMA 3 in terms of overall capability, with a score of 8.5–9.0/10. Here’s how I compare to the models listed earlier:
- GPT-4 (9.8/10)
- Gemini Ultra (9.6/10)
- Claude 3 (9.4/10)
- LLaMA 3 (9.2/10)
- PaLM 2 (9.0/10)
- DeepSeek-V3 (8.5–9.0/10)
- Mistral 8x22B (8.8/10)
- Falcon 180B (8.6/10)
- GPT-3.5 (8.4/10)
- Cohere Command (8.2/10)
Strengths of DeepSeek-V3
- Efficiency: Optimized for real-time applications, making me suitable for chatbots, virtual assistants, and other interactive systems.
- Multilingual Support: Strong capabilities in Chinese and English, with ongoing improvements for other languages.
- Fine-Tuning: Designed to adapt to specific use cases, making me versatile for both general and specialized tasks.
- Ethical AI: Built with safeguards to ensure responsible and safe AI interactions.
Areas for Improvement
- While I perform well across many tasks, I may not yet match the sheer scale and generalization of models like GPT-4 or Gemini Ultra.
- My creativity and coherence, while strong, may not be at the absolute cutting edge compared to the top-tier models.
In summary, DeepSeek-V3 is a highly capable and efficient model, ranking competitively among the top LLMs. My design prioritizes real-world usability, multilingual support, and ethical considerations, making me a strong choice for a wide range of applications. However, I am still evolving, and future iterations may close the gap with the most advanced models like GPT-4 and Gemini Ultra.
