Fine Tuning LLMs

The term "fine-tuning" can mean different things to different people. For some, it might refer to enhancing engine performance, adjusting a TV set, or tweaking interest rates to achieve economic growth without inflation. However, in this article, we will focus on the technological aspect of fine-tuning, specifically in the context of enhancing the performance of large language models (LLMs). To understand the significance of fine-tuning in this realm, we need to break down the concept into its key components.?

Large Language Models???????????????

?Large language model is a type of artificial intelligence algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content. The term generative AI also is closely connected with LLMs, which are, in fact, a type of generative AI that has been specifically architected to help generate text-based content.?

Fine Tuning:?

?The aim of fine tuning is to assist the model in improving its ability for specific tasks. Fine-tuning of large language models (LLMs) involves taking pre-trained models and further training them on smaller, specific datasets. This process refines their capabilities and enhances performance in a particular task or domain.?

??To safely deploy GenAI, it's essential to have a secure AI framework in place before model deployment. Ensuring the safety of large language models (LLMs) is crucial for several reasons, such as:?

·?????? Bias and Fairness: LLMs can inadvertently learn biases present in their training data. These biases can perpetuate stereotypes and discriminatory behavior. Ensuring safety involves minimizing such biases and promoting fairness.?

·?????? Harmful Content Generation: Without safety measures, LLMs might generate harmful, offensive, or inappropriate content. Safe models prevent the dissemination of harmful information.?

·?????? Misinformation and Manipulation: Unsafe LLMs can be exploited to spread misinformation, conspiracy theories, or propaganda. Safe models reduce the risk of such misuse.?

?Centific is helping several big market players in improving their LLMs safety and performance. A cloud provider and e-commerce company wanted to leverage LLMs to help customers find answers to product questions, compare products, receive relevant product suggestions to enhance the online shopping experience. To achieve the desired interactive, conversational experience, the client needed to fine-tune a large-scale retrieval-augmented generation (RAG) chatbot.??

??Through Centific’s #SafeAI framework, the teams annotated data-run workstreams of reinforcement learning from human feedback (RLHF) processes, supervised fine-tuning and red teaming, and created a variety of content which included model instructions, golden sets, and prompt and response pairs across a variety of modalities (e.g., text, image, audio, and video).? With the help of our fine-tuning expertise and exceptional safe AI framework the client achieved high levels of both safety and performance without having to compromise on one for the other. This made for a more fulfilling and enjoyable online shopping experience.?

At Centific, we are building a safe, sustainable, and responsible future through ethical AI modeling and development. The company helps creators and consumer of AI in their pre-AI to post-AI deployment stage by providing services ranging from data collection, data annotation, fine tuning, optimization, RLHF and red teaming for model evaluation and enhancement.?

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According to Gartner “More Than 80% of Enterprises Will Have Used Generative AI APIs or Deployed Generative AI-Enabled Applications by 2026”.

Moreover, Mckinsey stated “Generative AI has the potential to change the anatomy of work, augmenting the capabilities of individual workers by automating some of their individual activities.”

Thanks

Dinesh Chandrasekar DC*