Related papers: Testing the Reliability of ChatGPT for Text Annotation and Classification: A Cautionary Remark

Testing the Reliability of ChatGPT for Text Annotation and Classification: A Cautionary Remark

URL: http://arxiv.org/abs/2304.11085v1
Date: Mon, 17 Apr 2023 00:41:19 GMT
Title: Testing the Reliability of ChatGPT for Text Annotation and Classification: A Cautionary Remark
Authors: Michael V. Reiss
Abstract summary: This study investigates the consistency of ChatGPT's zero-shot capabilities for text annotation and classification. Results show consistency in ChatGPT's classification output can fall short of scientific thresholds for reliability.
Score: 0.0
License: http://creativecommons.org/licenses/by-sa/4.0/
Abstract: Recent studies have demonstrated promising potential of ChatGPT for various text annotation and classification tasks. However, ChatGPT is non-deterministic which means that, as with human coders, identical input can lead to different outputs. Given this, it seems appropriate to test the reliability of ChatGPT. Therefore, this study investigates the consistency of ChatGPT's zero-shot capabilities for text annotation and classification, focusing on different model parameters, prompt variations, and repetitions of identical inputs. Based on the real-world classification task of differentiating website texts into news and not news, results show that consistency in ChatGPT's classification output can fall short of scientific thresholds for reliability. For example, even minor wording alterations in prompts or repeating the identical input can lead to varying outputs. Although pooling outputs from multiple repetitions can improve reliability, this study advises caution when using ChatGPT for zero-shot text annotation and underscores the need for thorough validation, such as comparison against human-annotated data. The unsupervised application of ChatGPT for text annotation and classification is not recommended.

Related papers

Exploring the Capability of ChatGPT to Reproduce Human Labels for Social Computing Tasks (Extended Version) [26.643834593780007]
We investigate the extent to which ChatGPT can annotate data for social computing tasks. ChatGPT exhibits promise in handling data annotation tasks, albeit with some challenges. We propose GPT-Rater, a tool to predict if ChatGPT can correctly label data for a given annotation task.
arXiv Detail & Related papers (2024-07-08T22:04:30Z)
Exploring ChatGPT's Capabilities on Vulnerability Management [56.4403395100589]
We explore ChatGPT's capabilities on 6 tasks involving the complete vulnerability management process with a large-scale dataset containing 70,346 samples. One notable example is ChatGPT's proficiency in tasks like generating titles for software bug reports. Our findings reveal the difficulties encountered by ChatGPT and shed light on promising future directions.
arXiv Detail & Related papers (2023-11-11T11:01:13Z)
Chatbots Are Not Reliable Text Annotators [0.0]
ChatGPT is a closed-source product which has major drawbacks with regards to transparency, cost, and data protection. Recent advances in open-source (OS) large language models (LLMs) offer alternatives which remedy these challenges.
arXiv Detail & Related papers (2023-11-09T22:28:14Z)
Is ChatGPT Involved in Texts? Measure the Polish Ratio to Detect ChatGPT-Generated Text [48.36706154871577]
We introduce a novel dataset termed HPPT (ChatGPT-polished academic abstracts) It diverges from extant corpora by comprising pairs of human-written and ChatGPT-polished abstracts instead of purely ChatGPT-generated texts. We also propose the "Polish Ratio" method, an innovative measure of the degree of modification made by ChatGPT compared to the original human-written text.
arXiv Detail & Related papers (2023-07-21T06:38:37Z)
ChatLog: Carefully Evaluating the Evolution of ChatGPT Across Time [54.18651663847874]
ChatGPT has achieved great success and can be considered to have acquired an infrastructural status. Existing benchmarks encounter two challenges: (1) Disregard for periodical evaluation and (2) Lack of fine-grained features. We construct ChatLog, an ever-updating dataset with large-scale records of diverse long-form ChatGPT responses for 21 NLP benchmarks from March, 2023 to now.
arXiv Detail & Related papers (2023-04-27T11:33:48Z)
To ChatGPT, or not to ChatGPT: That is the question! [78.407861566006]
This study provides a comprehensive and contemporary assessment of the most recent techniques in ChatGPT detection. We have curated a benchmark dataset consisting of prompts from ChatGPT and humans, including diverse questions from medical, open Q&A, and finance domains. Our evaluation results demonstrate that none of the existing methods can effectively detect ChatGPT-generated content.
arXiv Detail & Related papers (2023-04-04T03:04:28Z)
Comparing Abstractive Summaries Generated by ChatGPT to Real Summaries Through Blinded Reviewers and Text Classification Algorithms [0.8339831319589133]
ChatGPT, developed by OpenAI, is a recent addition to the family of language models. We evaluate the performance of ChatGPT on Abstractive Summarization by the means of automated metrics and blinded human reviewers.
arXiv Detail & Related papers (2023-03-30T18:28:33Z)
Exploring ChatGPT's Ability to Rank Content: A Preliminary Study on Consistency with Human Preferences [6.821378903525802]
ChatGPT has consistently demonstrated a remarkable level of accuracy and reliability in terms of content evaluation. A test set consisting of prompts is created, covering a wide range of use cases, and five models are utilized to generate corresponding responses. Results on the test set show that ChatGPT's ranking preferences are consistent with human to a certain extent.
arXiv Detail & Related papers (2023-03-14T03:13:02Z)
Is ChatGPT a Good NLG Evaluator? A Preliminary Study [121.77986688862302]
We provide a preliminary meta-evaluation on ChatGPT to show its reliability as an NLG metric. Experimental results show that compared with previous automatic metrics, ChatGPT achieves state-of-the-art or competitive correlation with human judgments. We hope our preliminary study could prompt the emergence of a general-purposed reliable NLG metric.
arXiv Detail & Related papers (2023-03-07T16:57:20Z)
Can ChatGPT Understand Too? A Comparative Study on ChatGPT and Fine-tuned BERT [103.57103957631067]
ChatGPT has attracted great attention, as it can generate fluent and high-quality responses to human inquiries. We evaluate ChatGPT's understanding ability by evaluating it on the most popular GLUE benchmark, and comparing it with 4 representative fine-tuned BERT-style models. We find that: 1) ChatGPT falls short in handling paraphrase and similarity tasks; 2) ChatGPT outperforms all BERT models on inference tasks by a large margin; 3) ChatGPT achieves comparable performance compared with BERT on sentiment analysis and question answering tasks.
arXiv Detail & Related papers (2023-02-19T12:29:33Z)

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