Leveraging topic modeling to align user queries with document themes, enhancing the relevance and contextual accuracy of recommendations in an Natural Language Processing (NLP)-based system.
As the capabilities in Large Language Models (LLM), such as ChatGPT and Llama, continue to increase, a growing area of research revolves around adapting semantic reasoning to these systems. While these models do a great job providing responses grounded in predictions based on prior human knowledge, the issues arising with hallucinations, generic answers, as well as answers that don’t fulfill the users request are still common. Recommendation systems are parallel to LLMs in how they provide recommendations based on user input. Today, we will look at further enhancements in recommendation responses when adding additional metadata of the topics within a query and how they align with the data used to create a response.
This research is important because it could ultimately lead to enhancements in the semantic depth of large language models (LLMs) by incorporating human-like abilities to infer overarching topics inherent in a body of information.
