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Retrieval-Augmented Generation model

What is a Retrieval-Augmented Generation model?

In recent years, large language models (LLMs) have seen significant innovations and improvements in how they function and produce content. However, with this technology, there still could be some tweaks in how models can produce more authoritative responses with cited sources to boot. Retrieval-Augmented Generation was introduced to help the accuracy and access models have to external sources.

How does Retrieval Augmented Generation Work?

Retrieval-Augmented Generation (RAG) is a method of enhancing Gen AI functionality, specifically large language models. Language models are granted a number of parameters allowing them to read normal human writing patterns. This is sufficient for quickly answering basic prompts, but is severely limited in how it can respond to queries with either deeper nuance or meaning, providing more extensive answers, and otherwise deeper answers.

It acts as a link to external sources provided by the model users. Any works that are cited can then be placed as footnotes within the model’s generated content for the users to inspect and investigate further to validate the Gen AI’s response. Because of the added layer of accuracy, models can provide much more in-depth answers resulting in less confusion and fewer hallucinations. Surprisingly, RAGs also result in models that are both easier to set up and less expensive to maintain and provide sources for. These can be interconnected with multiple datasets using different data types and mediums, and have already garnered sufficient attention from multiple tech giants for the potential they offer.

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How Retrieval-Augmented Generation Works

A couple of models that covered this functionality previously existed.  Retrieval-based models are built to retrieve responses or information from a predefined set of responses based on the input query.  These models are great for archive models as they excel at drawing relevant information from a repository of existing knowledge and responses with little need to restructure anything.  Comparatively, generative models can construct new content over having to rely strictly on predetermined responses.  Typically built on neural networks trained on massive datasets to comprehend better and imitate human text.

Retrieval-augmented generation models, by comparison, are an amalgamation of these two model types.  This approach integrates the former model’s retrieval mechanism with the latter’s natural language component.  By extension, this model is comparatively flexible, having both a physical library of content to access to provide tangible and related facts to bolster its answer with the capacity to restructure it to best fit the user’s prompt.

Hallucinations with Retrieval Augmented Generation Works
When an LLM receives a question or prompt of any form in a natural language, the AI model passes it on to another model internally to convert the query into a numeric value for the first model to understand.  This numeric value relates to an index of available data to draw from.  The model then retrieves the matched query or queries and converts them back into natural language.  The database the model draws from can be a predefined knowledge base, a select set of documents, or any external source that the model can find if available.  Any additional sources that are the most relevant will also be incorporated as additional context.
The generative part of the model then restructures the gathered information with the initial prompt from the user into a single response.  The model will need to be extensively trained on datasets with input-output examples, adjusting the model parameters until it generates outputs that are not only coherent and relevant to the user input, but capable of doing so optimally.  During generation, the model uses decoding strategies to plan the most optimal method of writing its response.  Models can be trained to have a more dynamic approach to different contexts.  Weights can be adjusted for the retrieved information based on the input for a flexible and adaptable approach.

Conclusion

People’s most significant concern with Gen AI is plagiarism and an inability to properly cite sources if any content produced by a model is used. Despite the recent developments made to Gen AI models, hallucinations and other errors are still common in the outputs. By more closely integrating sources and source repositories into Gen AI models, this can sharply increase the accuracy models have and provide tangible sources they draw their conclusions and outputs from.

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Dolan Cleary

Dolan Cleary

I am a recent graduate from the University of Wisconsin - Stout and am now working with AllCode as a web technician. Currently working within the marketing department.

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