# A Comparative and Experimental Study on Automatic Question Answering Systems and its Robustness against Word Jumbling

Source: [A Comparative and Experimental Study on Automatic Question Answering Systems and its Robustness against Word Jumbling](<https://www.sota2.com/research/paper/68fa714b1e209131cea5081b>)

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

Data updated: 2025-10-26T02:03:39.970Z

Authors: Shashidhar Reddy Javaji, Haoran Hu, Sai Sameer Vennam, Vijaya Gajanan Buddhavarapu

Published: 2023-11-27T03:17:09.000Z

## Abstract

Question answer generation using Natural Language Processing models is ubiquitous in the world around us. It is used in many use cases such as the building of chat bots, suggestive prompts in google search and also as a way of navigating information in banking mobile applications etc. It is highly relevant because a frequently asked questions (FAQ) list can only have a finite amount of questions but a model which can perform question answer generation could be able to answer completely new questions that are within the scope of the data. This helps us to be able to answer new questions accurately as long as it is a relevant question. In commercial applications, it can be used to increase customer satisfaction and ease of usage. However a lot of data is generated by humans so it is susceptible to human error and this can adversely affect the model's performance and we are investigating this through our work

## 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/2311.15513>)

[PDF](<https://arxiv.org/pdf/2311.15513.pdf>)