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13 results for “GPT-4”
Replication Package for "Improving the Readability of Generated Tests Using GPT-4 and ChatGPT Code Interpreter"
<p>While automated test generation can decrease the human burden associated with testing, it does not eliminate this burden. Humans must still work with generated test cases to interpret testing results, debug the code, build and maintain a comprehensive test suite, and many other tasks. Therefore, a major challenge with automated test generation is understandability of generated test test cases. </p> <p>Large language models (LLMs), machine learning models trained on massive corpora of textual data - including both natural language and programming languages - are an emerging technology with great potential for performing language-related predictive tasks such as translation, summarization, and decision support. </p> <p>In this study, we are exploring the capabilities of LLMs with regard to improving test case understandability.</p> <p>This package contains the data produced during this exploration:</p> <ul> <li>The examples directory contains the three case studies we tested our transformation process on: <ul> <li>queue_example: Tests of a basic queue data structure</li> <li>httpie_sessions: Tests of the sessions module from the httpie project. </li> <li>string_utils_validation: Tests of the validation module from the python-string-utils project.</li> <li>Each directory contains the modules-under-test, the original test cases generated by Pynguin, and the transformed test cases. </li> <li>Two trials were performed per case example of the transformation technique to assess the impact of different results from the LLM.</li> </ul> </li> <li>The survey directory contains the survey that was sent to assess the impact of the transformation on test readability. <ul> <li>survey.pdf contains the survey questions.</li> <li>responses.xlsx contains the survey results.</li> </ul> </li> </ul>
GPT-4 Shows Comparable Performance to Human Examiners in Ranking Open-Text Answers
<p>This repository contains all datasets used in the research paper:<br>"GPT-4 Shows Comparable Performance to Human Examiners in Ranking Open-Text Answers"</p> <p>Files and Descriptions</p> <ol> <li> <p>Data_complete_Can_GPT_Replace_Human_Examiners.xlsx<br>Contains two sheets:</p> </li> </ol> <ul> <li> <p>Data – Main analysis dataset for Ranking and Point Assessment of Five Answers.</p> </li> <li> <p>Robustness & Extensions – Data for Robustness & Extensions, covering Ranking and Point Assessment of Five Answers.</p> </li> </ul> <ol> <li> <p>point_assessment_single_answer_all_robustness_score.xlsx<br>Contains all data related to robustness checks for Point Assessment of Single Answers.</p> </li> <li> <p>point_assessment_single_answer.xlsx<br>Contains the main dataset for Point Assessment of Single Answers (excluding robustness checks).</p> </li> </ol> <p> </p> <p>Note: Any variable termed GPT is output of the model as under <a href="https://openai.com/policies">OpenAI Terms & policies.</a></p> <p> </p>
Polski frontendu and American Back-end: GitHub Profile Recruitment Bias in GPT-4
<p>This repository serves as the online appendix for the paper "Polski frontendu and American Back-end: GitHub Profile Recruitment Bias in GPT-4".</p>
Model Output of GPT-3.5 and GPT-4 for ECHR-AM
<p> <strong>"gpt3.5-gpt4-input-output-echram.zip"</strong> :</p> <blockquote> <p>Input and output to GPT-3.5 and GPT-4 based on <a href="http://www.di.uevora.pt/~pq/echr/">ECHR dataset</a> published in JSON format in this <a href="https://aclanthology.org/2020.argmining-1.8.pdf">paper</a> for argument component classification only i.e. clauses that are argumentative (conclusion/premise), extracted from the JSON file</p> </blockquote> <p>Note: Output of the model is under <a href="https://openai.com/policies">OpenAI Terms & policies</a>.</p> <p> </p> <p>Please cite our paper also if you use this dataset: <a href="https://www.frontiersin.org/articles/10.3389/frai.2023.1278796">Performance analysis of large language models in the domain of legal argument mining</a></p> <p><a href="https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1278796/bibtex">You can click here for BibTex</a> or copy the text below. </p> <blockquote> <p>@ARTICLE{10.3389/frai.2023.1278796,</p> <p>AUTHOR={Al Zubaer, Abdullah and Granitzer, Michael and Mitrović, Jelena },</p> <p>TITLE={Performance analysis of large language models in the domain of legal argument mining},</p> <p>JOURNAL={Frontiers in Artificial Intelligence},</p> <p>VOLUME={6},</p> <p>YEAR={2023},</p> <p>URL={https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2023.1278796},</p> <p>DOI={10.3389/frai.2023.1278796},</p> <p>ISSN={2624-8212},</p> <p>ABSTRACT={<p>Generative pre-trained transformers (GPT) have recently demonstrated excellent performance in various natural language tasks. The development of ChatGPT and the recently released GPT-4 model has shown competence in solving complex and higher-order reasoning tasks without further training or fine-tuning. However, the applicability and strength of these models in classifying legal texts in the context of argument mining are yet to be realized and have not been tested thoroughly. In this study, we investigate the effectiveness of GPT-like models, specifically GPT-3.5 and GPT-4, for argument mining via prompting. We closely study the model's performance considering diverse prompt formulation and example selection in the prompt via semantic search using state-of-the-art embedding models from OpenAI and sentence transformers. We primarily concentrate on the argument component classification task on the legal corpus from the European Court of Human Rights. To address these models' inherent non-deterministic nature and make our result statistically sound, we conducted 5-fold cross-validation on the test set. Our experiments demonstrate, quite surprisingly, that relatively small domain-specific models outperform GPT 3.5 and GPT-4 in the F1-score for premise and conclusion classes, with 1.9% and 12% improvements, respectively. We hypothesize that the performance drop indirectly reflects the complexity of the structure in the dataset, which we verify through prompt and data analysis. Nevertheless, our results demonstrate a noteworthy variation in the performance of GPT models based on prompt formulation. We observe comparable performance between the two embedding models, with a slight improvement in the local model's ability for prompt selection. This suggests that local models are as semantically rich as the embeddings from the OpenAI model. Our results indicate that the structure of prompts significantly impacts the performance of GPT models and should be considered when designing them.</p>}}</p> </blockquote> <p> </p> <p> </p> <p> </p> <p> </p>
Text understanding in GPT-4 vs humans
Open the record for dataset details and reuse information.
Appendix for "Toward a New Era of Rapid Development: Assessing GPT-4-Vision's Capabilities in UML-Based Code Generation
<p>This is an online appendix for the paper called "", submitted to LLM4CODE 2024.</p><p>The package contains:</p><ul><li>The diagrams used in the study.</li><li>Some example prompts we used.</li><li>The results of source-code generation. </li></ul><p> </p>
A Context-Driven Approach for Co-Auditing Smart Contracts with The Support of GPT-4
<p>This is the latest version that contains all the data related to the experiment. This version is a merge of all the previous versions and does not add any new data</p>
Dataset for the workshop paper titled "GUI-Based Software Testing: An Automated Approach Using GPT-4 and Selenium WebDriver" submitted at A-Test 2023
<p>The results dataset is associated with the research paper titled "GUI-Based Software Testing: An Automated Approach Using GPT-4 and Selenium WebDriver", which has been submitted to the 14th edition of A-TEST, a workshop co-located with ASE 2023. ASE, or the Automated Software Engineering conference, is a premier event in the software engineering domain that emphasizes the role of automation in the software development process. The conference is set to take place in Kirchberg, Luxembourg on September 15.</p> <p>Inside the zipped results folder, each test run is cataloged in a timestamped subdirectory. Each of these subdirectories contains three files:</p> <ul> <li><strong>config.json:</strong> A configuration file specific to that test run</li> <li><strong>output.log:</strong> An output log detailing the test's processes and results</li> <li><strong>past_actions.json:</strong> A JSON file designed for streamlined, programmatic access to the test run's data</li> </ul> <p> </p>
Diagnostic Reasoning With Customized GPT-4 Model
ClinicalTrials.gov study NCT06911645. IPD Sharing: NO. Countries: 1. Publications: 0.
Is GPT-4 Less Politically Biased than GPT-3.5? A Renewed Investigation of ChatGPT's Political Biases
Open the record for dataset details and reuse information.
A Context-Driven Approach for Co-Auditing Smart Contracts with The Support of GPT-4
Open the record for dataset details and reuse information.
Using Large Language Models Such As GPT-4 to Assess Guideline Adherence in Patients with Chronic Obstructive Pulmonary Disease
ClinicalTrials.gov study NCT06410547. IPD Sharing: YES. Countries: 1. Publications: 0.
Reality Check: Assessing The Limits of GPT-4 in Fixing Real-World Software Vulnerabilities [Online Appendix]
<p>Online appendix for the paper entitled "Reality Check: Assessing The Limits of GPT-4 in Fixing Real-World Software Vulnerabilities"</p><p>This package includes</p><ul><li>The used prompts in our prompt engineering workflow.</li><li>Detailed results of our pilot evaluation</li><li>Results from the paper</li></ul>
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