Large Language Models / EndPoints
RAG Improves LLMs Significantly and Equally
Retrieval-Augmented Generation (RAG) significantly enhances the performance of Large Language Models (LLMs) in various GenAI applications. RAG boosts the quality of LLM responses by up to 13%* (Reference Es at el., response accuracy, measured by the "faithfulness" metric). This improvement is consistent even with information within the original training domain of the LLM.
The effectiveness of RAG increases with the amount of data available for search. Such extensive data availability enables LLMs to deliver more factually correct results. Interestingly, this improvement is not limited to high-end models like GPT-4. Comparable performance levels can also be achieved with other models, such as Mistral tiny.
In summary, using RAG with ample data improves the results of GenAI applications, regardless of the LLM used. This approach opens up the possibility of using a range of LLMs and offers flexibility in terms of cost and resource requirements. (Source)
Conclusion and Experiences
When using smaller language models (LLMs) for Retrieval-Augmented Generation (RAG) systems, there are several advantages, particularly in terms of speed, accuracy, and flexibility:
Speed and Efficiency: Smaller LLMs have significantly lower latency compared to larger models. This results in faster response times, which can be crucial for applications like chatbots. The time to the first token (TTFT) and time per output token (TPOT) are typically lower in smaller models.
Precision in Information Retrieval: When using RAG systems, it's necessary to break down large text corpora into smaller, manageable chunks. Smaller chunks allow for more precise information retrieval as they can capture more specific concepts. This can be particularly advantageous when searching for relevant information in large data volumes.
Adaptability and Flexibility: Smaller models offer greater flexibility in adapting to specific use cases. For example, finely tuned smaller models can perform as well or even better than larger models like GPT-4 in certain task areas. This opens up opportunities for customized solutions tailored to the needs and requirements of a particular application area.
Enhancing User Experience: Using RAG in conjunction with smaller LLMs can improve user experience by providing faster and more accurate responses to queries. This is particularly important in interactive applications where quick response times and accuracy are crucial.
Data Privacy and Security: Smaller LLMs used in a RAG system can help improve data privacy and security. Since sensitive data can be kept on-premises, it's easier to maintain control over data access and usage.
Overall, smaller LLMs in combination with RAG systems offer an efficient, adaptable, and precise solution for a wide range of applications, especially where quick response times and specific information needs are required.
Operation of the LLMs
mistral-swiss
Operated in Switzerland in the data center of our partner Exoscale, which meets the following standards: Finma compliant, ISO 9001:2015, ISO 27001:2013, PCI DSS 3.2, SOC-1 Type II, SOC-2 Type II. No logging or storing of prompts!
mistral-tiny, mistral-small, mistral-medium
The models are operated on servers in the European Union and are suitable for non-sensitive data with anonymization.
gpt-3.5-turbo-1106, gpt-4-1106-preview, gpt-4
The models are operated in the USA. Information on Security and Privacy
Platform and Anonymization Service
The platform is exclusively operated in Switzerland in the data center of our partner Exoscale, which meets the following standards: Finma compliant, ISO 9001:2015, ISO 27001:2013, PCI DSS 3.2, SOC-1 Type II, SOC-2 Type II.
Comparison of Performance
Here you will find a list of benchmarks for Large Language Models (LLMs), including MMLU, HellaSwag, ARC Challenge, Winogrande, GMS 8k, and MT Bench. These benchmarks are crucial for assessing the performance and versatility of language models in various scenarios.
MMLU (MultiModal Language Understanding) tests the understanding of models in a multimodal context, while HellaSwag assesses the ability of models to meaningfully complete sentences. The ARC Challenge is a challenging test for scientific understanding and problem-solving skills. Winogrande focuses on understanding pronouns in texts, a
key aspect of natural language understanding. GMS 8k (Generalized Model Scoring 8k) and MT Bench provide specialized tests for evaluating language generation and translation.
Our analysis aims to provide a comprehensive picture of the strengths and weaknesses of different language models by comparing them across these diverse benchmarks. This allows developers and users to make better-informed decisions about which model is best suited for their specific applications.
Benchmarks
| Mistral-tiny / swiss | GPT-3.5 | Mistral-small | Mistral-medium | GPT 4 | |
|---|---|---|---|---|---|
| MMLU (MCQ in 57 subjects) | 63.0% | 70.0% | 70.6% | 75.3% | 86.4% |
| HellaSwag (10-shot) | 83.1% | 85.5% | 86.7% | 88.0% | 95.3% |
| ARC Challenge (25-shot) | 78.1% | 85.2% | 85.8% | 89.9% | 96.3% |
| WinoGrande (5-shot) | 78.0% | 81.6% | 81.2% | 88.0% | 87.5% |
| GSM-8K (5-shot) | 36.5% | 57.1% | 58.4% | 66.7% | 97% |
| MT Bench (for Instruct models) | 7.61 | 8.32 | 8.30 | 8.61 | 9.32 |
Comparing the Performance of Language Models: An Overview of Leading Benchmarks
In the world of language models, performance evaluation is of crucial importance. To develop a comprehensive understanding of the capabilities of different models, it is important to compare them across a range of benchmarks. These benchmarks test various aspects of artificial intelligence, from understanding to problem-solving abilities. Here we provide an overview of some of the most important benchmarks:
MMLU (Massive Multitask Language Understanding): Assesses a model's understanding across a wide range of topics and disciplines, to evaluate how well a model comprehends complex texts and draws conclusions from them.
HellaSwag: Focused on predicting text completions in various scenarios, this test measures how well a model can generate creative and plausible continuations for given text snippets.
ARC Challenge (AI2 Reasoning Challenge): Tests the logical reasoning and problem-solving ability of a model, a great method to assess a model's ability to answer complex questions.
Winogrande: As a test for understanding Winograd-schema questions, Winogrande examines a model's ability to recognize and interpret subtle semantic differences in sentences.
GMS 8K (Grade School Math 8K): Tests mathematical skills and understanding of basic mathematical concepts, an indicator of how well a model can handle numerical data and logical problems.
MT-Bench: Specifically developed for evaluating machine translation models, this benchmark assesses the effectiveness and accuracy of a model in translating texts between different languages.
By evaluating a language model across these benchmarks, we can gain a detailed picture of its capabilities and limitations, which is crucial in selecting the most suitable models for specific applications and tasks.
INFO
Performance benchmarks may change. (As of January 28, 2024) Sources: https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard
https://docs.mistral.ai/platform/endpoints/
https://huggingface.co/spaces/gsaivinay/open_llm_leaderboard