# Speeding Up Question Answering Task of Language Models via Inverted Index

Source: [Speeding Up Question Answering Task of Language Models via Inverted Index](<https://www.sota2.com/research/paper/68fa6dba1e209131cea3ba08>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:41:22.038Z

Authors: Xiang Ji, Yesim Sungu-Eryilmaz, Elaheh Momeni, Reza Rawassizadeh

Published: 2022-10-24T19:59:17.000Z

## Abstract

Natural language processing applications, such as conversational agents and their question-answering capabilities, are widely used in the real world. Despite the wide popularity of large language models (LLMs), few real-world conversational agents take advantage of LLMs. Extensive resources consumed by LLMs disable developers from integrating them into end-user applications. In this study, we leverage an inverted indexing mechanism combined with LLMs to improve the efficiency of question-answering models for closed-domain questions. Our experiments show that using the index improves the average response time by 97.44%. In addition, due to the reduced search scope, the average BLEU score improved by 0.23 while using the inverted index.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2210.13578>)

[PDF](<https://arxiv.org/pdf/2210.13578.pdf>)