# Keyword Augmented Retrieval: Novel framework for Information Retrieval integrated with speech interface

arXiv: https://arxiv.org/abs/2310.04205 · HTML: https://mlsanity.com/paper/2310.04205/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 6 Oct 2023.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Finetuned Language Models Are Zero-Shot Learners](https://mlsanity.com/paper/2109.01652/) (2021)
- [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023)
- [SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models](https://mlsanity.com/paper/2303.08896/) (2023)
- [A Deep Relevance Matching Model for Ad-hoc Retrieval](https://mlsanity.com/paper/1711.08611/) (2017)
- [Unlocking Context Constraints of LLMs: Enhancing Context Efficiency of LLMs with Self-Information-Based Content Filtering](https://mlsanity.com/paper/2304.12102/) (2023)

## Built on by (most cited first, 11 of 11)

- [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.06211/) (2024)
- [Knowledge Graph-Guided Retrieval Augmented Generation](https://mlsanity.com/paper/2502.06864/) (2025)
- [Evaluating the Efficacy of Open-Source LLMs in Enterprise-Specific RAG Systems: A Comparative Study of Performance and Scalability](https://mlsanity.com/paper/2406.11424/) (2024)
- [LAG: Logic-Augmented Generation from a Cartesian Perspective](https://mlsanity.com/paper/2508.05509/) (2025)
- [Introducing a new hyper-parameter for RAG: Context Window Utilization](https://mlsanity.com/paper/2407.19794/) (2024)
- [Optimizing and Evaluating Enterprise Retrieval-Augmented Generation (RAG): A Content Design Perspective](https://mlsanity.com/paper/2410.12812/) (2024)
- [KnowsLM: A framework for evaluation of small language models for knowledge augmentation and humanised conversations](https://mlsanity.com/paper/2504.04569/) (2025)
- [COS-Mix: Cosine Similarity and Distance Fusion for Improved Information Retrieval](https://mlsanity.com/paper/2406.00638/) (2024)
- [i-LAVA: Insights on Low Latency Voice-2-Voice Architecture for Agents](https://mlsanity.com/paper/2509.20971/) (2025)
- [CultureVo: The Serious Game of Utilizing Gen AI for Enhancing Cultural Intelligence](https://mlsanity.com/paper/2407.20685/) (2024)
- [Osiris: A Lightweight Open-Source Hallucination Detection System](https://mlsanity.com/paper/2505.04844/) (2025)

## Abstract (arXiv)

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.