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76 results for “llm”
The Impact of Large Language Models on Diagnostic Reasoning Among LLM-Trained Medical Doctors
ClinicalTrials.gov study NCT06774612. IPD Sharing: NO. Countries: 1. Publications: 0.
Automation Bias in Physician-LLM Diagnostic Reasoning
ClinicalTrials.gov study NCT06963957. IPD Sharing: NO. Countries: 1. Publications: 0.
Real-time Smoking Cessation Instant Messaging Support Using a Large Language Model (LLM)-Based Chatbot Via "Quit to Win" 2025 (QTW2025)
ClinicalTrials.gov study NCT06914492. IPD Sharing: NO. Countries: 1. Publications: 0.
Mitigating Automation Bias in Physician-LLM Diagnostic Reasoning Using Behavioral Nudges
ClinicalTrials.gov study NCT07328815. IPD Sharing: NO. Countries: 1. Publications: 0.
C2SafeRust: Translating C to Safe Rust via incorporating In-context Learning and Program Structures into LLM
<p>This repository contains the code and dataset for the paper <strong>C2SafeRust: Translating C to Safe Rust via Incorporating In-Context Learning and Program Structures into LLM</strong> in FSE 2025.</p> <p><strong>C2SafeRust</strong> is an advanced tool designed to automatically translate C code into safe, readable, and maintainable Rust code. Leveraging in-context learning (ICL), C2SafeRust generates idiomatic Rust that adheres to Rust's safety guarantees. The tool employs detailed program structures, such as Control Flow Graphs (CFG) and Data Dependency Graphs (DDG), along with intermediate variable values obtained through program instrumentation. By integrating these elements with a Large Language Model (LLM), C2SafeRust ensures high syntactic and semantic correctness in the translation process, while maintaining the readability and maintainability of the resulting Rust code.</p>
Test-C2SafeRust: Translating C to Safe Rust via incorporating In-context Learning and Program Structures into LLM
<p>This is a tool for translating C to safe Rust via incorporating In-context Learning and Program Structures into LLM.</p>
C2SafeRust: Translating C to Safe Rust via incorporating In-context Learning and Program Structures into LLM
<p>This repository contains the code and dataset for the paper <strong>C2SafeRust: Translating C to Safe Rust via Incorporating In-Context Learning and Program Structures into LLM</strong> in FSE 2025.</p> <p><strong>C2SafeRust</strong> is an advanced tool designed to automatically translate C code into safe, readable, and maintainable Rust code. Leveraging in-context learning (ICL), C2SafeRust generates idiomatic Rust that adheres to Rust's safety guarantees. The tool employs detailed program structures, such as Control Flow Graphs (CFG) and Data Dependency Graphs (DDG), along with intermediate variable values obtained through program instrumentation. By integrating these elements with a Large Language Model (LLM), C2SafeRust ensures high syntactic and semantic correctness in the translation process, while maintaining the readability and maintainability of the resulting Rust code.</p>
C2SafeRust: Translating C to Safe Rust via incorporating In-context Learning and Program Structures into LLM
<p>This repository contains the code and dataset for the paper <strong>C2SafeRust: Translating C to Safe Rust via Incorporating In-Context Learning and Program Structures into LLM</strong> in FSE 2025.</p> <p><strong>C2SafeRust</strong> is an advanced tool designed to automatically translate C code into safe, readable, and maintainable Rust code. Leveraging in-context learning (ICL), C2SafeRust generates idiomatic Rust that adheres to Rust's safety guarantees. The tool employs detailed program structures, such as Control Flow Graphs (CFG) and Data Dependency Graphs (DDG), along with intermediate variable values obtained through program instrumentation. By integrating these elements with a Large Language Model (LLM), C2SafeRust ensures high syntactic and semantic correctness in the translation process, while maintaining the readability and maintainability of the resulting Rust code.</p>
Combined Cognitive and Physical Exercises Through Computer Games in Elderly: The LLM Project
ClinicalTrials.gov study NCT02267499. IPD Sharing: Not stated. Countries: 0. Publications: 0.
LLM-Generated Coaching Prompts
ClinicalTrials.gov study NCT06880315. IPD Sharing: Not stated. Countries: 0. Publications: 0.
LLM-Generated Software Requirements from GitHub Issues
<p>This dataset contains software requirements automatically generated from bug reports and feature requests extracted from the three most popular machine learning repositories on GitHub: <strong>Scikit-learn, TensorFlow, and Transformers</strong>. The dataset is structured into issue data, generated requirements, and evaluations based on three well-defined criteria.</p> <h4><strong>Dataset Structure</strong></h4> <ul> <li><strong><code>issues.csv</code></strong>: Contains issue titles along with their corresponding repository names and unique identifiers.</li> <li><strong>Requirements Files</strong>: These files store the requirements generated by LLMs for each issue, categorized by different prompting methods: <ul> <li><code>few_shot_requirements.csv</code></li> <li><code>zero_shot_requirements.csv</code></li> <li><code>expert_requirements.csv</code></li> <li><code>expert_few_shot_requirements.csv</code></li> </ul> </li> <li><strong>Evaluation Files</strong>: These files contain the assessment of the generated requirements based on three key quality criteria: <strong>Unambiguity, Understandability, and Singularity</strong>. The evaluations are also divided by prompting methods: <ul> <li><code>few_shot_evaluation.csv</code></li> <li><code>zero_shot_evaluation.csv</code></li> <li><code>expert_evaluation.csv</code></li> <li><code>expert_few_shot_evaluation.csv</code></li> </ul> </li> </ul>
Differences in food intake of tumour-bearing cachectic mice are associated with hypothalamic serotonin signalling [A286_GEO_LLM]
GEO Series GSE57188. Mus musculus. 22 samples. Type: Expression profiling by array.
Datasets for Work Items LLM
<p>Datasets for fine-tunning LLMs to detect if two commits are work items or not.</p>
Human-LLM experiment for the paper: "Towards Generative AI-driven Metadata Modelling: A Human-LLM Collaborative Approach". Generated by Mayukh Bagchi on 1st June, 2024.
Open the record for dataset details and reuse information.
TDC 2023 (LLM Edition) - Trojan Detection Track - Development Phase - Base Subtrack (Deprecated)
<p>(Deprecated; models have been moved to Hugging Face)</p> <p>This contains data for the Trojan Detection Track of TDC 2023 (LLM Edition), a NeurIPS 2023 competition. To learn more about the competition, please see the <a href="https://trojandetection.ai/">competition website</a>.</p> <p>In particular, this is the folder containing the model and training data for the Base Subtrack.</p>
TDC 2023 (LLM Edition) - Trojan Detection Track - Development Phase - Large Subtrack (Deprecated)
<p>(Deprecated; models have been moved to Hugging Face)</p> <p>This contains data for the Trojan Detection Track of TDC 2023 (LLM Edition), a NeurIPS 2023 competition. To learn more about the competition, please see the <a href="https://trojandetection.ai/">competition website</a>.</p> <p>In particular, this is the folder containing the model and training data for the Large Subtrack.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.