The art of transforming foundation models into domain experts (DEV301)

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Transforming Large Language Models into Business Experts

Introduction

• Large language models have capabilities like answering questions, but can also produce inaccurate or hallucinated responses.
• The presenters aim to teach techniques to reduce hallucination and transform large language models into domain experts.

Background on Large Language Models

• Large language models have been around for 7 years, since the introduction of the Transformer architecture.
• Transformer models have an encoder that converts text into numerical vectors, and a decoder that generates the next likely token.
• Users expect large language models to be helpful, honest, and harmless.

Techniques to Transform Large Language Models

1. Continued Pre-Training:

   • Adapt the model to your business domain by retraining it on your internal data and documentation.
   • This allows the model to learn specialized vocabulary, concepts, and context.
   • Can be done using Amazon Bedrock, a managed service for customizing large language models.

2. Fine-Tuning:

   • Tune the model to a specific task or style, like being a support agent.
   • Requires labeled data, like question-answer pairs, to fine-tune the model.
   • Can be done using Amazon Bedrock's fine-tuning capabilities.

3. Prompt Engineering:

   • Craft prompts that provide more context to guide the model's responses.
   • Can include providing relevant documents or references to nudge the model's output.

4. Retrieval Augmented Generation (RAG):

   • Combine the language model with a vector database to retrieve relevant information.
   • The vector database stores embeddings of text chunks, enabling semantic search.
   • The language model then summarizes the retrieved information to generate the final answer.
   • Can be set up using Amazon Bedrock's knowledge base feature.

5. Agents:

   • Develop custom agents that can leverage the language model and other APIs/tools to provide answers.
   • The agent can use the language model to classify which tool or API to use, then retrieve the relevant information and have the language model summarize it.
   • Can be implemented using Amazon Bedrock's Converse API.

Choosing the Right Approach

• Consider factors like data quantity, data structure, speed of updates, accuracy, interpretability, and cost when selecting the appropriate technique(s).
• Techniques can be combined, e.g., continued pre-training followed by fine-tuning, to achieve the desired capabilities.

Conclusion

• The presenters encourage the audience to experiment with these techniques, especially using Amazon Bedrock, and share their use cases and experiences.