AI Accuracy Score and How to Increase It

Mar 1, 2024 | General

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Understanding AI Accuracy Scoring

Understanding the intricacies of AI technology, particularly in the context of language models like ChatGPT or Gemini, involves delving into the world of Large Language Models (LLMs). LLMs, as defined in this comprehensive guide on AI and Machine Learning Terminology, are systems trained on vast datasets, enabling them to generate human-like text based on given prompts.

 LLMs and how they work:

  • LLMs are a type of AI: They are trained on a massive dataset of text and code, which allows them to generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. https://en.wikipedia.org/wiki/Wikipedia:Large_language_models
  • Understanding how they respond to your questions: When you ask a question to an LLM, it first analyzes the question to identify the key concepts and the relationships between them. Then, it searches its internal database of text and code for patterns that match the question. Finally, it uses these patterns to generate a response that is both relevant and coherent.

Why data is critical for LLM training:

  • Importance of data for training LLMs: The data used to train an LLM is essential for its ability to perform tasks. The more data an LLM is trained on, the better it will be able to understand and respond to complex questions. However, the data can also introduce biases and inaccuracies, making it challenging to achieve high accuracy.
  • AI Accuracy Score: To measure an LLM’s performance, we can use an AI Accuracy Score (which ranges from 0 to 1). This score is calculated as the ratio of accurate predictions to the total number of predictions. It is important to note that achieving a perfect score of 1 is nearly impossible, as language is nuanced and can be interpreted in multiple ways.

Supplementing LLMs for better performance:

  • Fine-tuning: This is a technique where an LLM is further trained on a specific dataset of text or code relevant to a particular domain or task. This helps the LLM to improve its performance on that specific task.
  • RAG (Retrieval-Augmented Generation): This is another technique used to improve the accuracy of LLMs. In RAG, the LLM first retrieves relevant information from a database of text and code, and then uses this information to generate a response to the query.

To summarize the AI Accuracy Score, it is calculated as:

 

AI Accuracy Score = number of accurate predictions / total number of predictions

 

At Vizaport, we leverage RAG to optimize our AI models. While implementing RAG is beneficial, Vizaport acknowledges the nuanced role of structured training data. The AI Accuracy Score is not solely dependent on LLM selection or model fine-tuning; the preparation and structuring of input data significantly influence outcomes. As the industry evolves, experts specializing in data preparation and prompt creation are becoming instrumental in achieving higher AI Accuracy Scores.

 AI Accuracy Score

 

AI Accuracy Score Image

In the image above, we illustrate the impact of various approaches on accuracy. 

  1. Low score – pointing solely to an LLM yields generalized responses.
  2. Medium score – utilizing a RAG with context allows for more specific training.
  3. High score – emphasizing a structured input data and implementing a feedback loop for continual training defines what we term AI Services

In summary, comprehending AI’s intricacies involves recognizing the balance between LLMs, fine-tuning, data structuring, and AI Services. The pursuit of higher AI Accuracy Scores necessitates a holistic approach, where each element plays a crucial role in shaping the capabilities of AI models. As the industry advances, the synergy of these components will be key to unlocking the full potential of AI technology.

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