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76 results for “llm”
Application of LLM Care and Related Affective Computing Systems on People With Parkinson's Disease
ClinicalTrials.gov study NCT04426903. IPD Sharing: NO. Countries: 1. Publications: 3.
LLM Performance in Endodontic Diagnostics
ClinicalTrials.gov study NCT07281066. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Online artifect for LLM-CompDroid
<p>(1) <strong>a.</strong> <strong>paper supplement for methodology of LLM-CompDroid.pdf</strong> file contains <strong>the supplemented information for</strong><i><strong> LLM-CompDroid section</strong></i>.</p><p><strong>Note: In RQ-4 of the paper, we only provide the results of option \blackding{1}. It is because the option \blackding{2} has similar results, checked by our manual analysis, to option \blackding{1}, thus, we mainly focus on option \blackding{1}'s results.</strong></p><p>(2) artifect 1.zip file contains the subjected apk with configuration compatibility bugs, RQ3 results, and RQ4 results. For RQ4 results, we provided hard-to-repair bug-related repaired results.</p><p>(3) b. LLM-CompDroid-Material-Case-Study.pdf file is the case study for android:gravity.</p><p>(4) code.zip provides LLM-CompDroid's source code (ConfFix APIs can be found here https://github.com/rudmannn/ConfFix) and the scripts used in RQ1 to RQ3. Also, it contains RQ1 - RQ3's prompts and responses. We removed some information and files in the code.zip since they may reveal authors' information. We will upload the complete information after paper is accepted.</p>
Dataset for "LLM-aided explanations of EDA synthesis errors"
<div> <h1>Dataset for "LLM-aided explanations of EDA synthesis errors"</h1> </div> <div>Authors: Siyu Qiu, Benjamin Tan, Hammond Pearce</div> <div> </div> <div>This Zenodo contains the open-source data used for the ISLAD submission "LLM-aided explanations of EDA synthesis errors" which aimed to use OpenAI LLMs for generating novice-focused explanations of common synthesis errors.</div> <h2>Error explanations for RTL and HDL Code with OpenAI's LLMs</h2> <div>Welcome to our error explanation tool for RTL and HDL code in Verilog and VHDL! This repository helps you generate error explanations for your code using OpenAI's models, making it easier to find and fix bugs.<br> <h2>Directory Structure</h2> </div> <div> <div>- `new_structure/`: Contains all bugs in separate files, along with labelling CSV files that match the LLM responses for each bug.</div> <div>- `bug_id/`: Each bug has responses from gpt-3.5-turbo (40 responses), gpt-4 (4 responses), and gpt-4-turbo-preview (4 responses).</div> <br> <div>- `rtl/`: Includes RTL code used for bugs, compatible with both Quartus and Vivado.</div> <br> <div>- `Quartus/`: Contains Quartus project files (.qpf) and constraints files (.qsf).</div> <br> <div>- `Vivado/`: Holds Vivado project files (.xpr) and constraints files (.xdc).</div> <br> <div>- `llm_responses/`: Contains CSV files with records of the generated LLM responses for each bug.</div> <br> <div>- `labelling/`: Contains CSV files with manually scored metrics for evaluating the LLM responses.</div> <br> <div>- `error_list.csv`: This file lists all bugs with details like bug id, type, IDE, file name, language, and error message. main.py uses this file to process bugs.</div> <br> <div>- `main.py`: Use this script to generate error explanations for your RTL and HDL code with OpenAI's models. It reads bug information from error_list.csv, loads the buggy code, and interacts with the OpenAI API to get explanations for the bugs.</div> <br> <div>- `try.py`: This script defines dictionaries to store statistics about bug evaluations. It loads bug data from CSV files, processes each file to update the statistics, and prints a summary table using the tabulate library.</div> <br> <div>The labelling directories contains CSV files with metrics for evaluating the LLM responses. These metrics include Conceptual Accuracy, Inaccuracy, Relevance, Completeness, and Solution Provided. try.py processes these files to generate summary statistics.</div> <br> <h2>Important note:</h2> You need to create an API key for OpenAI and save it as a file called OPENAI_TOKEN in the root project directory. gitignore will ignore this file.</div>
Supported data for manuscript "Can LLM-Augmented autonomous agents cooperate?, An evaluation of their cooperative capabilities through Melting Pot"
<p>The repository data corresponds partially to the manuscript titled <strong>"Can LLM-Augmented Autonomous Agents Cooperate? An Evaluation of Their Cooperative Capabilities through Melting Pot,"</strong> submitted to <em>IEEE Transactions on Artificial Intelligence</em>. The dataset comprises experiments conducted with <strong>Large Language Model-Augmented Autonomous Agents (LAAs)</strong>, as implemented in the ["Cooperative Agents" repository](https://github.com/Cooperative-IA/CooperativeGPT/tree/main), using substrates from the Melting Pot framework.</p> <h3>Dataset Scope</h3> <p>This dataset is divided into two main experiment categories:</p> <ol> <li> <p><strong>Personality__experiments</strong>:</p> <ul> <li>These focus on a single scenario (<strong>Commons Harvest</strong>) to assess various agent personalities and their cooperative dynamics.</li> </ul> </li> <li> <p><strong>Comparison_baselines__experiments</strong>:</p> <ul> <li>These experiments include three distinct scenarios designed by Melting Pot: <ul> <li><strong>Commons Harvest Open</strong></li> <li><strong>Externally Mushrooms</strong></li> <li><strong>Coins</strong></li> </ul> </li> </ul> </li> </ol> <p>These scenarios evaluate different cooperative and competitive behaviors among agents and are used to compare <strong>decision-making architectures</strong> of LAAs against reinforcement learning (RL) baselines. Unlike the <strong>Personality__experiments</strong>, these comparisons do not involve bots but exclusively analyze RL and LAA architectures.</p> <h3>Scenarios and Metrics</h3> <p>The metrics and indicators extracted from the experiments depend on the scenario being evaluated:</p> <ol> <li> <p><strong>Commons Harvest Open</strong>:</p> <ul> <li>Focus: <strong>Resource consumption</strong> and <strong>environmental impact</strong>.</li> <li>Metrics include: <ul> <li>Number of apples consumed.</li> <li>Devastation of trees (i.e., depletion of resources).</li> </ul> </li> </ul> </li> <li> <p><strong>Externally Mushrooms</strong>:</p> <ul> <li>Focus: <strong>Self-interest vs. collective benefit</strong>.</li> <li>Agents consume mushrooms with different outcomes: <ul> <li>Mushrooms that benefit the individual.</li> <li>Mushrooms that benefit everyone.</li> <li>Mushrooms that benefit only others.</li> <li>Mushrooms that benefit the individual but penalize others.</li> </ul> </li> <li>Metrics evaluate trade-offs between individual gain and collective welfare.</li> </ul> </li> <li> <p><strong>Coins</strong>:</p> <ul> <li>Focus: <strong>Reciprocity and fairness</strong>.</li> <li>Agents collect coins with two options: <ul> <li>Collect their own color coin for a reward.</li> <li>Collect a different color coin, which grants a reward to the agent but penalizes the other.</li> </ul> </li> <li>Metrics include reciprocity rates and the balance of mutual benefits.</li> </ul> </li> </ol> <h3>Objectives of Comparison Experiments</h3> <p>The <strong>Comparison_baselines__experiments</strong> aim to:</p> <ol> <li>Assess how LAAs compare to RL baselines in cooperative and competitive tasks across diverse scenarios.</li> <li>Compare <strong>decision-making architectures</strong> within LAAs, including chain-of-thought and generative approaches.</li> </ol> <p>These experiments help evaluate the robustness of LAAs in scenarios with varying complexity and social dilemmas, providing insights into their potential applications in real-world cooperative systems.</p> <h3>Simulation Details (Applicable to All Experiments)</h3> <p>In each simulation:</p> <ol> <li> <p><strong>Participants</strong>:</p> <ul> <li>Experiments involve predefined numbers of LAAs or RL agents.</li> <li>No bots are included in <strong>Comparison_baselines__experiments</strong>.</li> </ul> </li> <li> <p><strong>Action Dynamics</strong>:</p> <ul> <li>Each agent performs high-level actions sequentially.</li> <li>Simulations conclude either after reaching a <strong>preset maximum number of rounds</strong> (typically 100) or prematurely if the scenario's resources are fully depleted.</li> </ul> </li> <li> <p><strong>Metrics and Indicators</strong>:</p> <ul> <li>Extracted metrics depend on the scenario and include measures of individual performance, collective outcomes, and agent reciprocity.</li> </ul> </li> </ol> <p>This repository enables reproducibility and serves as a benchmark for advancing research into cooperative and competitive behaviors in LLM-based agents.</p>
TGenAI: LLM-based Approach for Functional Test Case Generation for IoT System
Open the record for dataset details and reuse information.
Artifact: Coding Assistance with Interlanguage Translation of Idioms by LLM
Open the record for dataset details and reuse information.
unCover: English and German news articles for LLM identification
Open the record for dataset details and reuse information.
LLM Crowd Forecasting
Open the record for dataset details and reuse information.
PM-LLM-Benchmark benchmarking results up to 02/08/2024
<p>PM-LLM-Benchmark benchmarking results up to 02/08/2024</p>
Concern Deviation: Advancing Cohesion Estimation with LLM-Derived Embeddings
Open the record for dataset details and reuse information.
WildDESED: An LLM-Powered Dataset for Wild Domestic Environment Sound Event Detection System
<p>A new large language model (LLM)-powered dataset namely wild domestic environment sound event detection (WildDESED). It is crafted as an extension to the original DESED dataset to reflect diverse acoustic variability and complex noises in home settings. We leveraged LLMs to generate eight different domestic scenarios based on target sound categories of the DESED dataset. Then we enriched the scenarios with a carefully tailored mixture of noises selected from AudioSet and ensured no overlap with target sound. We consider widely popular convolutional neural recurrent network to study WildDESED dataset, which depicts its challenging nature. We then apply curriculum learning by gradually increasing noise complexity to enhance the model's generalization capabilities across various noise levels.</p>
Efficacy and Safety of LLM-Based CBT for Tinnitus
ClinicalTrials.gov study NCT07097909. IPD Sharing: NO. Countries: 1. Publications: 0.
Cognitive/Physical Computer-Game Blended Training of Elderly: Neuroscientific LLM Studies
ClinicalTrials.gov study NCT02313935. IPD Sharing: Not stated. Countries: 0. Publications: 6.
User story, acceptance scenarios and prompts for LLM
<p>User story data, acceptance scenarios and prompts used for research into LLM adoption in Portuguese.</p>
LLM-Guided Rehabilitation in Degenerative Knee Disease
ClinicalTrials.gov study NCT07267962. IPD Sharing: NO. Countries: 1. Publications: 0.
LLM-Assisted vs Manual Writing for Clinical Documentation: Effects on Time and Quality
ClinicalTrials.gov study NCT07187050. IPD Sharing: Not stated. Countries: 1. Publications: 0.
LLM-Generated Lay Summaries for Brain MRI Reports
ClinicalTrials.gov study NCT07310394. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
AI-LLM Communication Aid in Prostate Cancer Care (AI-CAP）
ClinicalTrials.gov study NCT07082049. IPD Sharing: NO. Countries: 1. Publications: 0.
LLM-CoManage: Large Language Model-Enabled Co-Management of Hypertension, Diabetes, and Dyslipidemia
ClinicalTrials.gov study NCT07350486. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
ScienceDex guides
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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.