How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks

03/01/2023
by   Xuanting Chen, et al.
0

The GPT-3.5 models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks, showcasing their strong understanding and reasoning capabilities. However, their robustness and abilities to handle various complexities of the open world have yet to be explored, which is especially crucial in assessing the stability of models and is a key aspect of trustworthy AI. In this study, we perform a comprehensive experimental analysis of GPT-3.5, exploring its robustness using 21 datasets (about 116K test samples) with 66 text transformations from TextFlint that cover 9 popular Natural Language Understanding (NLU) tasks. Our findings indicate that while GPT-3.5 outperforms existing fine-tuned models on some tasks, it still encounters significant robustness degradation, such as its average performance dropping by up to 35.74% and 43.59% in natural language inference and sentiment analysis tasks, respectively. We also show that GPT-3.5 faces some specific robustness challenges, including robustness instability, prompt sensitivity, and number sensitivity. These insights are valuable for understanding its limitations and guiding future research in addressing these challenges to enhance GPT-3.5's overall performance and generalization abilities.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/12/2022

How Does Data Corruption Affect Natural Language Understanding Models? A Study on GLUE datasets

A central question in natural language understanding (NLU) research is w...
research
03/18/2023

A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models

GPT series models, such as GPT-3, CodeX, InstructGPT, ChatGPT, and so on...
research
11/15/2022

GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective

Pre-trained language models (PLMs) are known to improve the generalizati...
research
12/01/2021

Towards More Robust Natural Language Understanding

Natural Language Understanding (NLU) is a branch of Natural Language Pro...
research
01/03/2022

Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions

Recent natural language processing (NLP) techniques have accomplished hi...
research
08/21/2023

SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding

Large language models (LLMs) have shown impressive ability for open-doma...
research
04/14/2017

ShapeWorld - A new test methodology for multimodal language understanding

We introduce a novel framework for evaluating multimodal deep learning m...

Please sign up or login with your details

Forgot password? Click here to reset