Keyword Augmented Retrieval: Novel framework for Information Retrieval integrated with speech interface
This addresses cost and efficiency barriers for deploying language models in commercial search and chatbot applications, particularly when adding speech interfaces, though it appears incremental by optimizing existing methods.
The authors tackled the problem of high cost and hallucination in language model-based information retrieval from mixed data by developing a keyword-augmented framework that uses a smaller LLM to generate and cache keywords for context identification, reducing inference time and cost, and integrated a speech interface for seamless interaction.
Retrieving answers in a quick and low cost manner without hallucinations from a combination of structured and unstructured data using Language models is a major hurdle. This is what prevents employment of Language models in knowledge retrieval automation. This becomes accentuated when one wants to integrate a speech interface on top of a text based knowledge retrieval system. Besides, for commercial search and chat-bot applications, complete reliance on commercial large language models (LLMs) like GPT 3.5 etc. can be very costly. In the present study, the authors have addressed the aforementioned problem by first developing a keyword based search framework which augments discovery of the context from the document to be provided to the LLM. The keywords in turn are generated by a relatively smaller LLM and cached for comparison with keywords generated by the same smaller LLM against the query raised. This significantly reduces time and cost to find the context within documents. Once the context is set, a larger LLM uses that to provide answers based on a prompt tailored for Q\&A. This research work demonstrates that use of keywords in context identification reduces the overall inference time and cost of information retrieval. Given this reduction in inference time and cost with the keyword augmented retrieval framework, a speech based interface for user input and response readout was integrated. This allowed a seamless interaction with the language model.