Effective Techniques for Reducing Hallucinations in LLMs

Table of Contents

1. Introduction
2. Building a Retrieval-Based Question Answering System
   • Retrieval Part of the System
   • Combining a Prompt Template
   • Using Lang Chain to Extract an Answer
3. Demo of the Question Answering System
4. Observability with Weights and Biases
5. Summary of the Question Answering System
6. Next Steps
   • Accessing the Code
   • Documentation and Support
   • Commercial Version

Building a Retrieval-based Question Answering System

Retrieval-based question answering systems have gained popularity due to their ability to provide accurate answers by leveraging both search engine capabilities and the power of language models. This article focuses on building such a system using a combination of Lang Chain Rey, a small language model, and Weights and Biases, an evaluation tool.

Retrieval Part of the System

In the first part of the system, we Create an information source by extracting chunks from HTML files and converting them into embeddings. These embeddings serve as indexes for the search engine. When a query is received, it is also converted into embeddings, and similar chunks are retrieved as search results. These results are then returned to the user, similar to a traditional search engine.

Combining a Prompt Template

To overcome the limitations of language models, such as the lack of an answer or the tendency to generate incorrect answers, we introduce a prompt template. The prompt template is designed to provide Context and guide the language model in generating an answer that specifically addresses the query. The prompt template is defined by specifying the context and the desired behavior of the language model.

Using Lang Chain to Extract an Answer

Lang Chain comes into play when we connect the prompt template with the search engine results. By passing the prompt template and the search engine results to the Lang Chain pipeline, we allow the language model to summarize the information and generate a Relevant answer. This integration enables the system to overcome the limitations of language models and provide accurate answers based on the retrieved information.

Demo of the Question Answering System

Let's take a look at a demonstration of the retrieval-based question answering system. The system, implemented in code, utilizes Lang Chain Rey and Weights and Biases to showcase its capabilities. The source code includes modifications to the previous implementation, such as specifying the prompt, defining the system's context, and integrating support for Weights and Biases.

Observability with Weights and Biases

Weights and Biases, an evaluation tool, provides observability for the question answering system. By capturing intermediate values and query data, it allows us to gain insights into the system's performance and behavior. The integration of Weights and Biases enhances the overall monitoring and debugging capabilities of the system.

Summary of the Question Answering System

In summary, we have built a retrieval-based question answering system that combines the power of a search engine with a language model. By leveraging the prompt template and advanced embedding techniques, the system overcomes the limitations of language models such as ignorance and hallucination. The demonstration showcases the accuracy and effectiveness of the system in generating relevant and accurate answers.

Next Steps

To explore further and implement similar systems, You can access the code and examples from the repository provided. The documentation available at docs.ray.io offers comprehensive information and support for building your own systems. Additionally, the Ray community forums and Slack Channel provide a platform for discussion and collaboration. For those interested in a commercial version of the system with production-level reliability, please reach out to us, and we will gladly assist you.