Adversarial robustness of GPT-3.5 Turbo

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Perustieteiden korkeakoulu | Bachelor's thesis
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Date

2024-04-26

Department

Major/Subject

Data Science

Mcode

SCI3095

Degree programme

Aalto Bachelor’s Programme in Science and Technology

Language

en

Pages

34+12

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Abstract

The rapid advancement of artificial intelligence (AI) models has brought forth a critical need for a thorough examination of potential ethical and security concerns. However, many ethical issues regarding AI are being overlooked, including misinformation, bias, and accuracy. Within the scope of robustness, the study aims to assess the consistency of GPT’s output given the diversity of inputs. The primary objective is to construct a comprehensive framework for assessing the robustness of large language models (LLMs), with a specific emphasis on GPT 3.5. The research takes a proactive stance by developing experiments and methods using Python coding language, incorporating literature review and data analysis. This study answered the question how the algorithmic robustness of Large Language Models can, particularly GPT 3.5 turbo , be effectively assessed. By delving deeply into the assessment of robustness, the research seeks to address challenges associated with the widespread use of powerful language models, fostering a more secure, ethical, and transparent landscape in the field of artificial intelligence.

Description

Supervisor

Korpi-Lagg, Maarit

Thesis advisor

Jung, Alex

Keywords

GPT 3.5 turbo, adversarial robustness, artificial intelligence, large language model

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https://urn.fi/URN:NBN:fi:aalto-202405283906

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[kand] Perustieteiden korkeakoulu / SCI