Fine-Tuning LLMs: The Key to Build Better AI Agents

AI agents powered by fine-tuned large language models (LLMs) are revolutionizing the workplace by enhancing efficiency, decision-making, and creativity. These systems, trained on vast and specific datasets, allow businesses to automate complex tasks, from customer service to medical diagnostics. In 2024, about 67% of organizations reported adopting generative AI tools, with 58% actively experimenting with LLMs, showcasing a significant uptick in adoption compared to previous years.

Real-world applications include improving supply chain management, where businesses have seen revenue boosts of over 5%, and healthcare, where LLMs achieve diagnostic accuracy rates exceeding 80%​.

How do LLMs work?

Large Language Models (LLMs) work by learning patterns in human language from enormous amounts of text data. They are trained using a type of AI called deep learning, specifically a transformer architecture, which enables them to understand context, grammar, and meaning in text. During training, the model processes billions of sentences and adjusts its internal settings (parameters) to predict what word or phrase should come next in a sentence. This makes them capable of generating text, answering questions, and translating languages.

When you use an LLM, you give it an input or "prompt," and the model analyzes it based on the patterns it learned during training. For example, if you ask a question, the model draws on its knowledge to provide a relevant answer. LLMs use "tokens" (small pieces of text) to break down input and output, allowing them to handle large amounts of text. Advanced models, like GPT-4, can manage complex tasks like summarizing long documents or writing code.

AI Agents and How to Build Them?

An AI agent is a program that operates independently to complete tasks based on set goals. It gathers information, analyzes its environment, and makes decisions to achieve its objectives, often without much human involvement.

These agents also learn and improve over time by interacting with data provided and human feedback, making them more effective at handling complex tasks.

There are different types of AI agents, assistive agents that assist the users in their everyday specified tasks while autonomous agents can understand and respond to customer enquiries without human intervention. This is done by using an agent builder.

Assistive Agents Example: Voice Recognition Software, Reading Assistants, Text to Speech.

Autonomous Agents Example: Chatbots, Code Developers, Fraud Detection Agents.

Building an AI Agent might sound complex but it is not.

An AI Agent can be built in 6 main steps.

Step 1: Define purpose:

Clearly identify the goal of your AI agent and its intended functionality, such as task automation or generating content. Always have a clear picture in mind regarding the task or job you want to get done by your agent.

Step 2: Choose a Model:

Select an AI model like GPT, LLaMA, Gemini or Hugging Face based on your agent's requirements. Choosing a model completely depends on our requirements.

GPT (OpenAI's GPT Models):

Best for: General-purpose language tasks, conversational AI, content generation, and natural language understanding.

LLaMA (Meta’s LLaMA Models):

Best for: Research, custom fine-tuning, and high-performance NLP tasks on a smaller scale compared to GPT.

Gemini (Google’s Gemini Models):

Best for: Advanced, highly specialized tasks like multimodal AI (text, image, video), and Google Cloud integration.

Hugging Face (Transformers Library):

Best for: Custom model deployment, wide variety of pre-trained models, and easy integration into machine learning workflows.

BERT (Bidirectional Encoder Representations from Transformers)

Best for: Text classification, sentence-level tasks, and tasks requiring deep understanding of context in text.

Step 3: Prepare Data:

Gather the data your agent will use for training or fine-tuning. Clean the data by removing errors, duplicates, or missing values. Make sure the data is organized and labeled correctly for the task. The better the data, the more accurate your agent will be. A well-prepared dataset helps the agent learn effectively and improves its performance.

Step 4: Design the WorkFlow:

Plan how your agent will process inputs, make decisions, and generate outputs. Define how it will receive input, the steps it will take to analyze it, and how it will provide results. This includes mapping out the logic and the flow of information, ensuring everything runs smoothly.

Step 5: Implement and Test:

Build the agent using the chosen tools like Langchain, programming language like Python and then test it with various inputs to ensure it works as expected.

Always cross check the outputs being given by the AI agent to understand how well it performs. Belu/Rouge scores are used to evaluate the quality of generated text by comparing it to reference texts.

Step 6: Optimize and deploy:

Fine-tune the agent for performance, deploy it to the desired platform, and monitor its performance for continuous improvement.

Techniques for Adapting LLMs to Custom Tasks:

Fine-tuning and Prompt Engineering are two main steps to improve a LLMs performance for a particular task. This process turns general models into specialized ones. For example, LLMs may struggle with medical terms or customer queries due to a lack of specific data. By fine-tuning these models, providing them with relevant datasets and by performing prompt engineering techniques, organizations can improve accuracy and make them better suited for specialized tasks, enabling more accurate outputs.

There are several techniques to customize a LLMl:

1. Fine Tuning Models locally:

Fine-tuning large models like GPT-3 or LLaMA-3 70B needs powerful hardware, such as GPUs with 40GB+ VRAM and 128GB+ RAM, along with PyTorch. NVIDIA A100/V100 GPUs, often in multiples, are ideal. Large datasets with thousands of examples are crucial, though smaller domain-specific data can work with proper techniques. Efficient data preparation and optimization are key to better results.

2. In-Context Learning:

In-context learning allows the model to adapt to specific tasks by providing examples directly in the prompt, without retraining, offering the LLM a blueprint of what it needs to accomplish

3. Single-Shot and Few-Shot Inference:

Single-shot inference allows the model to perform tasks by learning from just one single example, but mainly relying on its general knowledge.

Few-shot inference lets the model learn from just a few examples, helping it generate relevant outputs without needing lots of data.

While few-shot is more accurate, single-shot is versatile and can handle many tasks without extra training.

4. PeFT:

PeFT (Parameter-Efficient Fine-Tuning) is a method that optimizes the fine-tuning process by only adjusting a small portion of the model's parameters. This makes it less resource-intensive and faster than traditional fine-tuning, allowing you to specialize large models without needing massive computational power. PeFT helps fine-tune models on specific tasks while maintaining efficiency. An example of PeFT is LoRA.

LoRA (Low-Rank Adaptation) is a specific PeFT technique that focuses on modifying only low-rank components of the model, reducing the number of parameters that need to be updated.

Hallucination in LLMs due to Fine Tuning.

Hallucinations happen when the model gets ambiguous inputs or when the fine-tuning data is too small or unbalanced. For instance, a model trained for medical tasks without enough clinical data might give wrong advice. Fine-tuning with new data is slower than improving its existing knowledge and can harm what it already knows.

Training too much on unfamiliar content increases the risk of hallucinations. To avoid this, fine-tuning should be done carefully, with well-balanced data that complements the model's original training. Proper evaluation is also important to ensure the model produces accurate and reliable outputs.

Simply put, the model can get confused by new information and start forgetting what it already knows.

Exhibit 1: Shows how accuracy changes during fine-tuning with 50% known and 50% unknown examples.

These findings are important for anyone building specialized LLM applications. Fine-tuning is helpful for improving the model’s performance for specific outputs. However, fine-tuning to make the model more accurate with new information often backfires and can have the opposite effect.

Conclusion:

In conclusion, fine-tuned LLMs are revolutionizing industries by enhancing efficiency, decision-making, and automation across various sectors. By training models on specialized datasets, businesses can create AI agents capable of handling complex tasks more effectively. However, the fine-tuning process must be done carefully to avoid issues like hallucination, which can arise from insufficient or poorly representative data. Despite these challenges, the continued development of AI agents and LLMs holds great potential for transforming the way businesses operate, making tasks more streamlined and personalized for users. As AI technology evolves, these agents will play an increasingly central role in shaping the future of work.