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

• URL: http://arxiv.org/abs/2303.00293v1
• Date: Wed, 1 Mar 2023 07:39:01 GMT
• Title: How Robust is GPT-3.5 to Predecessors? A Comprehensive Study on Language Understanding Tasks
• Authors: Xuanting Chen, Junjie Ye, Can Zu, Nuo Xu, Rui Zheng, Minlong Peng, Jie Zhou, Tao Gui, Qi Zhang, Xuanjing Huang
• Abstract summary: GPT-3.5 models have demonstrated impressive performance in various Natural Language Processing (NLP) tasks. However, their robustness and abilities to handle various complexities of the open world have yet to be explored. We show that GPT-3.5 faces some specific robustness challenges, including instability, prompt sensitivity, and number sensitivity.
• Score: 65.7949334650854
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• A Comprehensive Evaluation of Large Language Models on Mental Illnesses [0.8458496687170665]
  GPT-4 and Llama 3 exhibited superior performance in binary disorder detection, with accuracies reaching up to 85% on certain datasets. prompt engineering played a crucial role in enhancing model performance. Despite promising results, our analysis identified several challenges, including variability in performance across datasets and the need for careful prompt engineering.
  arXiv  Detail & Related papers  (2024-09-24T02:58:52Z)
• SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories [55.161075901665946]
  Super aims to capture the realistic challenges faced by researchers working with Machine Learning (ML) and Natural Language Processing (NLP) research repositories. Our benchmark comprises three distinct problem sets: 45 end-to-end problems with annotated expert solutions, 152 sub problems derived from the expert set that focus on specific challenges, and 602 automatically generated problems for larger-scale development. We show that state-of-the-art approaches struggle to solve these problems with the best model (GPT-4o) solving only 16.3% of the end-to-end set, and 46.1% of the scenarios.
  arXiv  Detail & Related papers  (2024-09-11T17:37:48Z)
• Taqyim: Evaluating Arabic NLP Tasks Using ChatGPT Models [6.145834902689888]
  Large language models (LLMs) have demonstrated impressive performance on various downstream tasks without requiring fine-tuning. Despite having a lower training proportion compared to English, these models also exhibit remarkable capabilities in other languages. In this study, we assess the performance of GPT-3.5 and GPT-4 models on seven distinct Arabic NLP tasks.
  arXiv  Detail & Related papers  (2023-06-28T15:54:29Z)
• GPT-3.5, GPT-4, or BARD? Evaluating LLMs Reasoning Ability in Zero-Shot Setting and Performance Boosting Through Prompts [0.0]
  Large Language Models (LLMs) have exhibited remarkable performance on various Natural Language Processing (NLP) tasks. In this paper, we examine the performance of GPT-3.5, GPT-4, and BARD models, by performing a thorough technical evaluation on different reasoning tasks.
  arXiv  Detail & Related papers  (2023-05-21T14:45:17Z)
• A Comprehensive Capability Analysis of GPT-3 and GPT-3.5 Series Models [71.42197262495056]
  GPT series models have gained considerable attention due to their exceptional natural language processing capabilities. We select six representative models, comprising two GPT-3 series models and four GPT-3.5 series models. We evaluate their performance on nine natural language understanding (NLU) tasks using 21 datasets. Our experiments reveal that the overall ability of GPT series models on NLU tasks does not increase gradually as the models evolve.
  arXiv  Detail & Related papers  (2023-03-18T14:02:04Z)
• Prompting GPT-3 To Be Reliable [117.23966502293796]
  This work decomposes reliability into four facets: generalizability, fairness, calibration, and factuality. We find that GPT-3 outperforms smaller-scale supervised models by large margins on all these facets.
  arXiv  Detail & Related papers  (2022-10-17T14:52:39Z)
• Artificial Text Detection via Examining the Topology of Attention Maps [58.46367297712477]
  We propose three novel types of interpretable topological features for this task based on Topological Data Analysis (TDA) We empirically show that the features derived from the BERT model outperform count- and neural-based baselines up to 10% on three common datasets. The probing analysis of the features reveals their sensitivity to the surface and syntactic properties.
  arXiv  Detail & Related papers  (2021-09-10T12:13:45Z)
• TextFlint: Unified Multilingual Robustness Evaluation Toolkit for Natural Language Processing [73.16475763422446]
  We propose a multilingual robustness evaluation platform for NLP tasks (TextFlint) It incorporates universal text transformation, task-specific transformation, adversarial attack, subpopulation, and their combinations to provide comprehensive robustness analysis. TextFlint generates complete analytical reports as well as targeted augmented data to address the shortcomings of the model's robustness.
  arXiv  Detail & Related papers  (2021-03-21T17:20:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.