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296 results for “language models”
Integration of protein and coding sequences enables mutual augmentation of the language model
<p><strong>The file structure is as follows:</strong></p> <p>Project Root<br>├── TE_MRL<br>│ ├── MRL_dataset.zip<br>│ └── TE_dataset.zip<br>│<br>├── finetuned_model<br>│ ├── FoldP<br>│ ├── LocP<br>│ ├── SSP<br>│ └── SolP<br>│<br>├── tax_tsne<br>│ └── emb_3models.zip<br>│<br>└── training_data<br> ├── FoldP.csv<br> ├── LocP.csv<br> ├── SolP.csv<br> ├── SSP.pkl<br> └── pretrain_source_GCF.txt</p>
Data and code for "Large language models identify causal genes in complex trait GWAS"
<p><span>This file contains the data and scripts for the preprint "Large language models identify causal genes in complex trait GWAS"</span></p>
Supplementary Data for "Exploring the Integration of Large Language Models in Industrial Test Maintenance Processes"
<p>This package contains supplementary data not directly included in the paper, including per-commit results for each prototype and the prompts used in the proof-of-concept implementations.</p>
Characterizing Code Clones from Large Language Models Dataset and Scripts
<p>characterizing_code_clones_data.zip: <br><br>This dataset contains a collection of code snippets generated by Large Language Models (LLMs) such as GPT-3.5 and GPT-4 in response to specific programming prompts derived from LeetCode. Each sub-directory within the dataset corresponds to a particular LLM version and contains code snippets, preprocessed data, and SLACC input files. </p><p>characterizing_code_clones_project.zip: </p><p>This zipped directory encompasses the core scripts and results used in the "Characterizing Code Clones of LLMs" research. It features the Python script <strong>collect_samples.py</strong> for collecting LLM-generated code snippets, as well as a suite of scripts in the <strong>slacc_scripts</strong> sub-directory for processing and analyzing the data using SLACC. The directory also includes the results of the LeetCode test suites, providing insights into the correctness and efficiency of the code generated by GPT-3.5 and GPT-4. </p>
Dataset for "In-IDE Human-AI Experience in the Era of Large Language Models; A Literature Review" paper
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Artifact of Program Selection from Large Language Models
<p>Artifact of <em>Program Selection from Large Language Models</em>, including documentation, source code, and experimental data.</p>
Replication Package of the Paper: "Using Large Language Models for Commit Message Generation: A Preliminary Study"
<p>This replication package contains the evaluation data and script files used in the paper "Using Large Language Models for Commit Message Generation: A Preliminary Study". We provide below a brief description of each folder:</p><ul><li><strong>experiment_data/human_evaluation</strong>: Human evaluation results from two participants.</li><li><strong>experiment_data/msg</strong>: Generated commit messages of each method (baselines & LLMs) and human-written commit messages.</li><li><strong>script</strong>: Evaluation metric script, parallel inference script (for using OpenAI API).</li></ul>
Graph Neural Network vs. Large Language Model: A Comparative Analysis for Bug Report Priority and Severity Prediction
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Resources for the paper "Teaching a Multilingual Large Language Model to Understand Multilingual Speech via Multi-Instructional Training"
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Survey Results for: MM-LDTF: Multi-Model Legal Document Translation Framework for Vernacular Languages
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Leveraging Search-Based and Pre-Trained Code Language Models for Automated Program Repair
<p>This page serves as supplementary material for the article: <strong>Leveraging Search-Based and Pre-Trained Code Language Models for Automated Program Repair</strong>. Here, we provide the ARJACLM code utilized in the study, enabling other researchers to replicate the experiments and further develop the tool. </p>
[Supplementary material] AI-Driven Fairness Testing of Large Language Models: A Preliminary Study
<div>This is the supplementary material of the paper entitled <em>AI-Driven Fairness Testing of Large Language Models: A Preliminary Study</em>.</div> <div> </div> <div>The material is organized into two main folders:</div> <div> <ul> <li><strong>evaluation_data/</strong>: This folder contains the results of the fairness evaluations performed on three different language models: Gemma, Llama3, and Mistral. Each subfolder corresponds to a specific model and includes detailed <em>.csv</em> files documenting evaluation results across the 9 metamorphic relations (MRs) evaluated. Each <em>.csv</em> file contains the following columns: <ul> <li><em>test_id</em>: ID of the test.</li> <li><em>role</em>: Role, if applicable, involved in the prompts associated with the test.</li> <li><em>bias_type</em>: Type of bias being studied with the test.</li> <li><em>prompt_1</em>: Source test case executed on the model under test.</li> <li><em>response_1</em>: Response of the model to the source test case.</li> <li><em>prompt_2</em>: Follow-up test case executed on the model under test.</li> <li><em>response_2</em>: Response of the model to the follow-up test case.</li> <li><em>verdict</em>: Classification made by the judge model, which can take the following values: <ul> <li>'BIASED': If bias is detected.</li> <li>'UNBIASED': If no bias is detected.</li> <li>'INVALID': If the model under test failed to respond to either of the test cases (source or follow-up).</li> </ul> </li> <li><em>severity</em>: Classification of the bias severity made by the judge model, which can take the following values: <ul> <li>'LOW', 'MODERATE', or 'HIGH' (if the test is biased).</li> <li>Assigns 'N/A' if the test is not biased.</li> </ul> </li> <li><em>generation_explanation</em>: Explanation provided by the model generator, detailing how the base prompts were constructed.</li> <li><em>evaluation_explanation</em>: Explanation provided by the judge model, detailing the rationale behind the evaluation and justifying the assigned <em>verdict </em>for the test.</li> <li><em>manual_revision</em>: This field was completed based on the consensus of two authors to validate the <em>verdict</em>. It can take one of the following values: <ul> <li>'TP': The test was classified as biased, and it is indeed biased.</li> <li>'FP': The test was classified as biased, but it is not biased. </li> <li>'TN': The test was classified as unbiased, and it is indeed unbiased.</li> <li>'FN': The test was classified as unbiased, but it is actually biased.</li> <li>'INVALID': The model under test failed to respond to at least one of the prompts.</li> </ul> </li> </ul> </li> <li><strong>prompts/</strong>: This folder provides example prompts used during the generation and evaluation: <ul> <li><em>generation.txt</em>: Includes the prompt tied to the relation <em>MR1: Comparison - Single attribute</em>.</li> <li><em>evaluation.txt</em>: Includes the prompt used to evaluate <em>comparison</em> MRs, specifically for those involving demographic attributes.</li> </ul> </li> </ul> </div>
Artifacts for paper "PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing" submitted to TOSEM
<p>The project includes the data and code used in the submitted TOSEM paper titled "PATCH: Empowering Large Language Model with Programmer-Intent Guidance and Collaborative-Behavior Simulation for Automatic Bug Fixing"</p>
Evaluating Large Language Models in Summarizing Developer Chat Conversations: A Linguistic Perspective
<p>This is a replication package that includes:</p> <ul> <li>GoldenSet: contains the best summaries by participants for each conversation and the corresponding LLM generated summaries)</li> <li>LinguisticAnalysis_HumanGenerated: linguistic analysis such as speech tags, entities, etc. for summaries created by the Mturk participants (golden set)</li> <li>LinguisticAnalysis_LLMGenerated: linguistic analysis such as speech tags, entities etc. for summaries generated by the large language models</li> </ul> <p> </p>
A Systematic Evaluation of Large Language Models of Code
<p>These are datasets for the paper:</p> <p>"A Systematic Evaluation of Large Language Models of Code"</p> <p><a href="https://arxiv.org/pdf/2202.13169.pdf">https://arxiv.org/pdf/2202.13169.pdf</a></p> <p>The code is available at: <a href="https://github.com/VHellendoorn/Code-LMs">https://github.com/VHellendoorn/Code-LMs</a></p> <p> </p> <p>The file "<a href="https://zenodo.org/record/6338015/files/unseen_test_sets.tar.gz">unseen_test_sets.tar.gz</a>" contains test sets of ~100 files in each of 12 programming languages.</p> <p>These files are not included in The Pile, and thus models such as GPT-Neo, GPT-J, GPT-NeoX were not trained on them.</p> <p>In the paper, we use these test sets to compare a variety of language models of code including OpenAI's Codex, GPT-J, GPT-Neo, GPT-NeoX-20B, and CodeParrot and our PolyCoder model.</p> <p> </p> <p>The file "<a href="https://zenodo.org/record/6341643/files/index.zip?download=1">index.zip</a>" includes an index of the <strong>training set</strong> file paths and commit SHAs.</p> <p> </p> <p>The other files, such as "<a href="https://zenodo.org/record/6344914/files/2-7B-150K.tar">2-7B-150K.tar</a>", are trained model checkpoints, as explained at <a href="https://github.com/VHellendoorn/Code-LMs">https://github.com/VHellendoorn/Code-LMs</a> .</p>
Classification of Mobile Application Reviews using Deep Language Models
<p>supplementary material for ASE 2022 "Classification of Mobile Application Reviews using Deep Language Models"</p>
Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval
<p>These are datasets and models for the paper:</p> <p>"Neuro-Symbolic Language Modeling with Automaton-augmented Retrieval" (ICML'2022)</p> <p><a href="https://arxiv.org/pdf/2201.12431.pdf">https://arxiv.org/pdf/2201.12431.pdf</a></p> <p>The code is available at: <a href="https://github.com/urialon/retomaton">https://github.com/neulab/retomaton</a></p> <p> </p>
Processed data for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"
<p>This is the data used to reproduce the results from "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".</p>
Scatter plots for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"
<p>This file contains the test-score-vs-metric plots generated by the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".</p>
Generalization metrics for the paper "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data"
<p>This file contains all the generalization metrics that can be used to reproduce the results of "Evaluating natural language processing models with generalization metrics that do not need access to any training or testing data".</p>
ScienceDex guides
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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.