# Automated Answer Validation using Text Similarity

Source: [Automated Answer Validation using Text Similarity](<https://www.sota2.com/research/paper/68fa71de1e209131cea53751>)

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

Data updated: 2025-10-26T02:14:27.865Z

Authors: Balaji Ganesan, Arjun Ravikumar, Lakshay Piplani, Rini Bhaumik, Dhivya Padmanaban, Shwetha Narasimhamurthy, Chetan Adhikary, Subhash Deshapogu

Published: 2024-01-13T07:13:08.000Z

## Abstract

Automated answer validation can help improve learning outcomes by providing appropriate feedback to learners, and by making question answering systems and online learning solutions more widely available. There have been some works in science question answering which show that information retrieval methods outperform neural methods, especially in the multiple choice version of this problem. We implement Siamese neural network models and produce a generalised solution to this problem. We compare our supervised model with other text similarity based solutions.

## 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/2401.08688>)

[PDF](<https://arxiv.org/pdf/2401.08688.pdf>)