Keyword Augmented Retrieval: Novel framework for Information Retrieval
integrated with speech interface
- URL: http://arxiv.org/abs/2310.04205v2
- Date: Sun, 29 Oct 2023 09:25:47 GMT
- Title: Keyword Augmented Retrieval: Novel framework for Information Retrieval
integrated with speech interface
- Authors: Anupam Purwar and Rahul Sundar
- Abstract summary: Retrieving answers in a quick and low cost manner without hallucinations using Language models is a major hurdle.
This is what prevents employment of Language models in knowledge retrieval automation.
For commercial search and chat-bot applications, complete reliance on commercial large language models (LLMs) like GPT 3.5 etc. can be very costly.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
Related papers
- Improving Retrieval in Sponsored Search by Leveraging Query Context Signals [6.152499434499752]
We propose an approach to enhance query understanding by augmenting queries with rich contextual signals.
We use web search titles and snippets to ground queries in real-world information and utilize GPT-4 to generate query rewrites and explanations.
Our context-aware approach substantially outperforms context-free models.
arXiv Detail & Related papers (2024-07-19T14:28:53Z) - Context-augmented Retrieval: A Novel Framework for Fast Information Retrieval based Response Generation using Large Language Model [0.0]
As the corpus of contextual information grows, the answer/inference quality of Retrieval Augmented Generation (RAG) based Question Answering (QA) systems declines.
This work solves this problem by combining classical text classification with the Large Language Model (LLM)
New approach Context Augmented retrieval (CAR) demonstrates good quality answer generation along with significant reduction in information retrieval and answer generation time.
arXiv Detail & Related papers (2024-06-24T07:52:05Z) - Redefining Information Retrieval of Structured Database via Large Language Models [10.117751707641416]
This paper introduces a novel retrieval augmentation framework called ChatLR.
It primarily employs the powerful semantic understanding ability of Large Language Models (LLMs) as retrievers to achieve precise and concise information retrieval.
Experimental results demonstrate the effectiveness of ChatLR in addressing user queries, achieving an overall information retrieval accuracy exceeding 98.8%.
arXiv Detail & Related papers (2024-05-09T02:37:53Z) - PromptReps: Prompting Large Language Models to Generate Dense and Sparse Representations for Zero-Shot Document Retrieval [76.50690734636477]
We propose PromptReps, which combines the advantages of both categories: no need for training and the ability to retrieve from the whole corpus.
The retrieval system harnesses both dense text embedding and sparse bag-of-words representations.
arXiv Detail & Related papers (2024-04-29T04:51:30Z) - Effective and Efficient Conversation Retrieval for Dialogue State Tracking with Implicit Text Summaries [48.243879779374836]
Few-shot dialogue state tracking (DST) with Large Language Models (LLM) relies on an effective and efficient conversation retriever to find similar in-context examples for prompt learning.
Previous works use raw dialogue context as search keys and queries, and a retriever is fine-tuned with annotated dialogues to achieve superior performance.
We handle the task of conversation retrieval based on text summaries of the conversations.
A LLM-based conversation summarizer is adopted for query and key generation, which enables effective maximum inner product search.
arXiv Detail & Related papers (2024-02-20T14:31:17Z) - Generative Context-aware Fine-tuning of Self-supervised Speech Models [54.389711404209415]
We study the use of generative large language models (LLM) generated context information.
We propose an approach to distill the generated information during fine-tuning of self-supervised speech models.
We evaluate the proposed approach using the SLUE and Libri-light benchmarks for several downstream tasks: automatic speech recognition, named entity recognition, and sentiment analysis.
arXiv Detail & Related papers (2023-12-15T15:46:02Z) - DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain
Question Answering over Knowledge Base and Text [73.68051228972024]
Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge.
Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
arXiv Detail & Related papers (2023-10-31T04:37:57Z) - Query Rewriting for Retrieval-Augmented Large Language Models [139.242907155883]
Large Language Models (LLMs) play powerful, black-box readers in the retrieve-then-read pipeline.
This work introduces a new framework, Rewrite-Retrieve-Read instead of the previous retrieve-then-read for the retrieval-augmented LLMs.
arXiv Detail & Related papers (2023-05-23T17:27:50Z) - Synergistic Interplay between Search and Large Language Models for
Information Retrieval [141.18083677333848]
InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections.
InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods.
arXiv Detail & Related papers (2023-05-12T11:58:15Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.