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Dataset results
36 results for “Language Models for Code”
Analyzing the Dependability of Large Language Models for Code Clone Generation.
<div> <p>data.zip: <br><br>This dataset includes a collection of ten LeetCode programming problems used in the study "Analyzing the Dependability of Large Language Models for Code Clone Generation". At the top level, you will find a CSV file containing all the initial LeetCode data. Each subdirectory at this level represents a specific LeetCode problem. Within these subdirectories, you will find the original solutions, their behaviors, the input corpus, as well as folders dedicated to various temperatures, models, and code cloning tasks. Additionally, within the "repeated" folder, you will find the original LLM-generated snippets, the preprocessed snippets with the snippet behavior, and the results.</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. The common folder was used to run the whole pipeline, the various parts of the pipeline are each in a folder as well as the various data analysis scripts! </p> <p> </p> </div>
Analyzing the Dependability of Large Language Models for Code Clone Generation
<p>data.zip: <br><br>This dataset includes a collection of ten LeetCode programming problems used in the study "Analyzing the Dependability of Large Language Models for Code Clone Generation". At the top level, you will find a CSV file containing all the initial LeetCode data. Each subdirectory at this level represents a specific LeetCode problem. Within these subdirectories, you will find the original solutions, their behaviors, the input corpus, as well as folders dedicated to various temperatures, models, and code cloning tasks. Additionally, within the "repeated" folder, you will find the original LLM-generated snippets, the preprocessed snippets with the snippet behavior, and the results.</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. The common folder was used to run the whole pipeline, the various parts of the pipeline are each in a folder as well as the various data analysis scripts! </p>
On Inter-dataset Code Duplication and Data Leakage in Large Language Models
<p>This dataset encompasses the sparse graph referenced in the publication titled "On Inter-dataset Code Duplication and Data Leakage in Large Language Models."</p> <p>This resource is a snapshot of the original <a href="https://github.com/Antolin1/code-inter-dataset-duplication">repository</a>, and the graph is preserved in the <em>interduplication.db</em> database. The schema of this database is easily understandable and is available in the original repository. Each code snippet is identified by a unique identifier (id_within_dataset) that corresponds to its identification within the dataset from which it was extracted. The complete datasets are stored in .jsonl files within their respective folders (e.g., python-150/data.jsonl, codetrans/data.jsonl, etc.).</p> <p> </p> <p> </p> <p> </p>
Artifacts for "On Hardware Security Bug Code Fixes By Querying Large Language Models""
<p>This repository contains the benchmarks and results obtained for the work "On Hardware Security Bug Code Fixes<br> By Querying Large Language Models".<br> Follow the README.md file for more information on how to use the tools yourself.</p>
Replication Package for "An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code"
<p>This repository contains the replication package for the paper <em>"</em>An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code" by Max Hort, Anastasiia Grishina, and Leon Moonen, accepted for publication in the 17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2023).</p> <p>The paper is deposited on arXiv, will be available later at the publisher's site (<a href="https://ieeexplore.ieee.org/Xplore/home.jsp">IEEE</a>), and a copy is included in this repository.</p> <p>The replication package is archived on Zenodo with DOI: <a href="https://doi.org/10.5281/zenodo.8058667">10.5281/zenodo.8058667</a>. The data is distributed under the CC BY 4.0 license.</p> <p> </p> <p><strong>Citation</strong></p> <p>If you build on this data or code, please cite this work by referring to the paper:</p> <pre><code>@inproceedings{hort2023:sharing, title = {An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code}, author = {Max Hort and Anastasiia Grishina and Leon Moonen}, booktitle = {17th ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM 2023)}, year = {2023}, publisher = {IEEE} note = {To appear. Pre-print on arXiv.} }</code></pre>
No More In-Context Learning? Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
<p>Data and models part of the replication package of the ICSE 24 submission entitled "<em>No More In-Context Learning? Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models</em>".</p>
[Replication Package] Evaluating the Impact of Post-Training Quantization on Large Language Models for Code Generation
<p>This repository contains scripts, datasets, and results of the work <em>"Evaluating the Impact of Post-Training Quantization on Large Language Models for Code Generation"</em></p> <p><strong>Scripts contained in this Zenodo repository can also be visualized at the following link: <a href="https://anonymous.4open.science/r/lowbit-quantization-D070/README.md">https://anonymous.4open.science/r/lowbit-quantization-D070/README.md</a><br></strong></p>
CodeEval: Pedagogy Based Benchmark Dataset for Evaluation Of Large Language Models Trained On Code
<p>Categories has been modified to 21 from 26.</p>
Data and code from: Large language models can segment narrative events similarly to humans.
<p>Data and code supporting the paper: Large language models can segment narrative events similarly to humans.</p>
CodeQual: A dataset for fine-tuning Large Language Models for code quality assessment task
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Advancing Automated Code Review Comment Generation Using Large Language Models
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Data and code for, "Large language models design sequence-defined macromolecules via evolutionary optimization"
<div> <pre># Codes and data for "Large language models design sequence-defined macromolecules via evolutionary optimization"<br><br>Note this repository contains codes and data files for the manuscript. This is a snapshot of the repository, frozen at the time of submission.<br><br># Codes<br><br>## LLM codes<br>- `run_claude.py` - the routine for performing LLM-based rollouts; intended for command line execution using argparse<br>- `message_utils.py` - utilities for constructing and parsing messages for LLM I/O<br>- `model_utils.py` - lightweight utilities for retrieving formatted predictions from the RNN ensemble<br>- `target_defs.py` - defines the sequence, locations, and natural language descriptions of the target structures<br>- `ask_about_oracle.ipynb` - asks the LLM to speculate about the nature of the optimization task<br><br>## other algorithms<br>- `active_learning.ipynb` - use EI acquisition with RF surrogate to label new sequences; includes an unused tokenization scheme<br>- `evolutionary_algorithm.ipynb` - use DEAP library to perform evolutionary optimization<br>- `random_sampling.ipynb` - sample sequences randomly from all possible sequences<br><br>## postprocessing<br>- `process_aggregated_logs.py` - reads data from the raw log files and prepares them for visualization<br>- `process_sample_rollouts.py` - reads data from the raw log files and prepares individual rollouts<br><br>## visualization<br>- `figure1b.ipynb` - renders panel b of Fig. 1<br>- `figure1efg.ipynb` - renders the last row of Fig. 1 (panels e-g)<br>- `figure2.ipynb` - renders all of Fig. 2<br>- `figure_si.ipynb` - renders Figs. S1 and S2<br>- `figure_md_validation.ipynb` - renders Fig. S3<br><br># Data files<br><br>- `prompts/`<br> - `prompt-scientific-v4.4.yml` - the full text of the scientific prompt, to be read by `run_claude.py`<br> - `prompt-oracle-v4.4.yml` - the full text of the oracle prompt, to be read by `run_claude.py`<br>- `models/` - the TorchScript RNN models used to make predictions<br>- `data/`<br> - `embeddings` - calculated embeddings for a collection of sequences from our prior work<br> - `llm-logs` - the raw logs obtained from the Claude 3.5 Sonnet LLM (other algorithms made to look like the LLM logs after the fact)<br> - `llm-logs-opus` - the raw logs obtained from the Claude 3.0 Opus LLM (used in the first draft of the article, replaced by Claude 3.5 Sonnet) <br> - `all-rollouts-kltd.csv` - postprocessed logs for all the rollouts using the "top $k < d^*$" metric<br> - `all-rollouts-topkd.csv` - postprocessed logs for all the rollouts using the "mean $d$ for top $k$" metric<br> - `sample-rollout-membranes-x-3.csv` - postprocessed logs for a single rollout replica, `x` = each algorithm type<br> - `snapshots` - png snapshots of MD simulation results at different locations in the manifold</pre> </div>
When Large Language Models Meet Fragile Code Completion Dataset
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Combining Large Language Models with Static Analyzers for Code Review Generation
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CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models
<p>This repository contains prompts generated by CodeIPPrompt, a platform used to assess potential intellectual property infringement risks associated with the output of code language models. The source code of the platform can be found at our GitHub repository: <a href="https://github.com/zh1yu4nyu/CodeIPPrompt">https://github.com/zh1yu4nyu/CodeIPPrompt</a>. Detailed information regarding the datasets, as well as usage instructions, can be found in the README.md file.</p> <p>The paper has been accepted by International Conference on Machine Learning (ICML) 2023. If you find this work helpful, please cite us as follows:</p> <pre><code class="language-markdown">@inproceedings{yu2023codeipprompt, title={CodeIPPrompt: Intellectual Property Infringement Assessment of Code Language Models}, author={Yu, Zhiyuan and Wu, Yuhao and Zhang, Ning and Wang, Chenguang and Vorobeychik, Yevgeniy and Xiao, Chaowei}, booktitle={International Conference on Machine Learning}, year={2023}, organization={PMLR} }</code></pre>
Exploring the Impact of the Output Format on the Evaluation of Large Language Models for Code Translation
<p>Please find the most up to date version of the dataset at<br>https://github.com/RISElabQueens/forge24-code-translation</p> <p> </p> <p>Thank you</p>
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Allen Brain Atlas
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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.