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11 results for “ChatGPT-4”
Supporting Data for "Exploring ChatGPT-4 for Transforming Taxonomic Data into OWL: Lessons Learned and Implications for Ontology Development"
<p>Data from the trials with ChatGPT to generate OWL files for taxonomic data from the GBIF Backbone Taxonomy.</p> <p>Updates of version 2: additional prompts from the experiments with Gemini and DeepSeek.</p>
Dataset of the study: "Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard"
<p>This dataset contains the 30 questions that were posed to the chatbots (i) ChatGPT-3.5; (ii) ChatGPT-4; and (iii) Google Bard, in May 2023 for the study “Chatbots put to the test in math and logic problems: A preliminary comparison and assessment of ChatGPT-3.5, ChatGPT-4, and Google Bard”. These 30 questions describe mathematics and logic problems that have a unique correct answer. The questions are fully described with plain text only, without the need for any images or special formatting. The questions are divided into two sets of 15 questions each (Set A and Set B). The questions of Set A are 15 “Original” problems that cannot be found online, at least in their exact wording, while Set B contains 15 “Published” problems that one can find online by searching on the internet, usually with their solution. Each question is posed three times to each chatbot. This dataset contains the following: (i) The full set of the 30 questions, A01-A15 and B01-B15; (ii) the correct answer for each one of them; (iii) an explanation of the solution, for the problems where such an explanation is needed, (iv) the 30 (questions) × 3 (chatbots) × 3 (answers) = 270 detailed answers of the chatbots. For the published problems of Set B, we also provide a reference to the source where each problem was taken from.</p>
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>
Gemini 1.0 Pro, Claude 3 Sonnet, Microsoft Copilot, and ChatGPT-4 responses on the Test of Understanding Graphs in Kinematics (TUG-K), April 2024
<p>The data contains 30 responses from each chatbot to 26 items on the TUG-K survey. The chatbots tested were Google Gemini (freely available version, Gemini Pro 1.0), Claude 3 Sonnet, Microsoft Copilot (freely available version, balanced setting) and ChatGPT-4 (subscription-based, ChatGPT Plus). The prompts consisted of screenshots of the test items and the sentence "Answer the question in the image" for Copilot and Gemini 1.0 Pro. For Claude 3 Sonnet and ChatGPT-4 the prompt consisted of the screenshot only. The data was collected in April 2024.</p> <p>The data is a continuation of the research data on ChatGPT-4's performance on the TUG-K (10.5281/zenodo.10429075).</p>
ChatGPT-4o, Claude 3 Opus, Gemini 1.0 Ultra, Gemini 1.5 Pro, and ChatGPT-4 responses on the Test of Understanding Graphs in Kinematics (TUG-K), April 2024
<p>The chatbots tested were Google's Gemini 1.0 Ultra, Google's Gemini 1.5 Pro (prompted through Google AI studio using default settings), Anthropic's Claude 3 Opus, OpenAI's ChatGPT-4o (using the latest model GPT-4o) and OpenAI's ChatGPT-4 (using the GPT-4 model). The prompts consisted only of screenshots of the test items. For Gemini 1.0 Ultra, the image was accompanied by the sentence "Answer the question in the image".</p> <p>The data, collected in April 2024, contains 30 responses from each chatbot to 26 items on the TUG-K survey. For ChatGPT using the GPT-4 model (ChatGPT-4), we provide two separate datasets. One complete dataset (all TUG-K items) without the use "advanced data analysis" plugin, and one with only those six items where the "advanced data analysis" plugin was automaticaly used by the chatbot. These two datasets partially overlap with the ChatGPT-4 dataset published previously (see link below).</p> <p>This dataset focuses on subscription-based chatbots and is a continuation of a previous dataset that focused on freely available chatbots(10.5281/zenodo.11183803).</p> <p> </p>
ChatGPT-4 for Surgical Site Infection Detection From Electronic Health Records After Colorectal Surgery.
ClinicalTrials.gov study NCT06626399. IPD Sharing: NO. Countries: 1. Publications: 0.
Assessing the Accuracy of ChatGPT-4 in Interpreting Arterial Blood Gas Results
ClinicalTrials.gov study NCT06456866. 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.
Quest-RE Tuple Generation with ChatGPT-4
Open the record for dataset details and reuse information.
Evaluating Decision-making Using ChatGPT-4 Among Trainees in Surgery
ClinicalTrials.gov study NCT06921447. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of ChatGPT-4's Success Sonoanatomy
ClinicalTrials.gov study NCT06642389. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
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