Streamlining AI Agent Development with Natural Language Reconciliation

In this article, we explore how large language models (LLMs) can be improved by retrieving information from vast, unstructured documents. These documents contain a wealth of knowledge that LLMs can use to generate more accurate and informative responses. We discuss several methods for retrieving information from these documents, including Dense Passage Retrieval (DPR) and embedding vector-based data formats.
To understand how LLMs work, imagine them as virtual assistants that can answer questions or create content based on the information they have been trained on. These training datasets typically contain a lot of text, but this text is often organized in a way that makes it difficult for LLMs to retrieve specific pieces of information quickly and efficiently. DPR methods help overcome this challenge by retrieving relevant passages from these documents, which can then be used to train the LLM.
One issue with DPR is that it can be computationally expensive, especially when dealing with very large documents or trillions of tokens (as in Borgeaud et al., 2022). To overcome this limitation, researchers have proposed embedding vector-based data formats, which represent words, phrases, or even entire documents as dense vectors in high-dimensional spaces. These vectors enable LLMs to efficiently and accurately retrieve necessary information without requiring DPR.
Another challenge is maintaining long-term memories in conversational agents. Zhong et al. (2023) identified this limitation in current LLM-based applications, which often rely on dense vector retrieval methods like DPR. To address this issue, researchers are exploring alternative approaches that can store information in a more efficient and effective way.
In summary, this article discusses how LLMs can be improved by retrieving information from vast documents, using DPR and embedding vector-based data formats. These methods enable LLMs to retrieve specific pieces of information quickly and efficiently, without relying on computationally expensive DPR methods. Additionally, researchers are exploring alternative approaches to maintain long-term memories in conversational agents. By leveraging these advances, we can create more accurate and informative language models that can help us communicate more effectively and make better decisions.