A Comparison of Instance Attribution Methods

Comparing Instance Attribution Methods to Baseline k-Nearest Neighbors Method

Bachelor thesis (2023)

Authors

E.J. de Kruif Electrical Engineering, Mathematics and Computer Science

Contributors

Avishek Anand Leibniz Universität (mentor)
L. Corti Web Information Systems - EEMCS (graduation committee member)
Lijun Lyu L3S (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2023 Evan de Kruif
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Published Date

03-02-2023

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In this research, a comparison between different Instance Attribution (IA) methods and k-Nearest Neighbors (kNN) via cosine similarity is conducted on a Natural Language Processing (NLP) machine learning model. The format in which the comparison is made is by way of a human survey and automated similarity comparisons of representative vectors. The goal of this is to judge and compare the effectiveness of each method’s results in the context of a human’s language understanding and ability to determine if a fact is true or not. Through this research, it was found that for results obtained on the same input, IA methods were preferred 32.5% more often than kNN. It is also shown that this preference is not linked to the similarity between the IA results and the kNN results. Through these findings, it can be seen that when understood through the lens of human comprehension, IA methods are much more effective at generating a set of influential training points from the model’s training dataset.