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43 results for “language generation”
Automated Programming Exercise Generation in the Era of Large Language Models
<p>Lecturers are increasingly attempting to use large language models (LLMs) to simplify and make the creation of exercises for students more efficient. Efforts are also being made to automate the exercise creation process in software engineering (SE) education. This study explores the use of advanced LLMs, including GPT-4 and LaMDA, for automated programming exercise creation in higher education and compares the results with related work using GPT-3.5-turbo. Utilizing applications such as ChatGPT, Bing AI Chat, and Google Bard, we identify LLMs capable of initiating different exercise designs. However, manual refinement is crucial for accuracy. Common error patterns across LLMs highlight challenges in complex programming concepts, while specific strengths in various topics showcase model distinctions. This research underscores LLMs' value in exercise generation, emphasizing the critical role of human supervision in refining these processes. Our concise insights cater to educators, practitioners, and other researchers seeking to enhance SE education through LLM applications.</p>
BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 3. Representation of the grammar G1 in the labelled graph G0 1
<p>If we take the labeled graph G0 1 given in Figure 3 and construct the stratified graph structure over (99) such that (100) we obtain (101), (102). </p> <p>In this paper, we proposed a new system for formal language generation by means of stratified graphs structures. This mechanism can generate languages of the first type and of the second type. More precisely, we propose a new system for formal language generation by means of a system of knowledge based on stratified graphs. We exemplified that, using an interpretation system specially defined for stratified graphs representations, a particular formal language can be obtained by means of the resulted accepted structured paths.</p>
BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 2. The representation of the rule
<p>In order to model these derivations in the stratified graph G, each production of the grammar will be represented in the labeled graph G0 by a direct arc of the form given in Figure 2.</p> <p> </p>
BRAIN Journal-Right-Linear Languages Generated in Systems of Knowledge Representation based on LSG-Right-Figure 1. The graphical representation of the morphism
<p>A morphism of partial algebras such that (30) and if (31), then (32) (see Figure 1). We obtain f(L) = T which means that “for every element of L the associated element of T is computed by the morphism f” (Ţăndăreanu, 2000).</p>
NLAS-multi: A Multilingual Corpus of Automatically Generated Natural Language Argumentation Schemes
<p>The multilingual corpus of natural language argumentation schemes (NLAS-multi) consists of 3,810 natural language argumentation schemes of which 1,893 are in English and 1,917 in Spanish. It has a total of 253,516 words distributed in 118,493 words in English and 135,023 words in Spanish. In terms of inferences, our corpus has a total of 7,964 (3,949 in English and 4,015 in Spanish). Furthermore, the NLAS-multi corpus contains a total of 23,781 conflict relations between arguments in the same topic.</p>
ETimeline: An Extensive Timeline Generation Dataset based on Large Language Model
<div> <div>Timeline generation is of great significance for a comprehensive understanding of the development of events over time. Its goal is to organize news chronologically, which helps to identify patterns and trends that may be obscured when viewing news in isolation, making it easier to track the development of stories and understand the interrelationships between key events. Timelines have appeared in many commercial products, but there is a noticeable lack of research in this field in academia, and existing datasets need improvement in terms of effectiveness and scale. We propose the ETimeline dataset, which contains over 13,000 news articles, covering 600 bilingual timelines across 23 news domains. We collected more than 120,000 news articles as a candidate news pool and used the large language model (LLM) Pipeline to enhance performance, ultimately obtaining the ETimeline, and the news pool data will also be provided. This work contributes to the advancement of timeline generation research and supports a wide range of tasks, including topic generation and event relationships. We believe that this dataset will serve as a catalyst for innovative research and bridge the gap between academia and industry in understanding the practical application of technology services.</div> </div>
Using ChatGPT to Generate Gendered Language
<p>Dataset for Gendered Language</p>
Geoparsing with Large Language Models: Leveraging the linguistic capabilities of generative AI to improve geographic information extraction
<h2>Geoparsing with Large Language Models</h2> <p>The .zip file included in this repository contains all the code and data required to reproduce the results from our paper. Note, however, that in order to run the OpenAI models, users will required an OpenAI API key and sufficient API credits.</p> <div> <h3>Data</h3> <p>The data used for the paper are in the <code>datasetst</code> and <code>results</code> folders.</p> <ul> <li> <p>**Datasets: **This contains the XML files (LGL and Geovirus) and Json files (News2024) used to benchmark the models. It also contains all the data used to fine-tune the gpt-3.5 model, the prompt templates sent to the LLMs, and other data used for mapping and data creation.</p> </li> <li> <p>**Results: **This contains the results for the models on the three datastes. The folder is separated by dataset, with a single <code>.csv</code> file giving the results for each model on each dataset separately. The <code>.csv</code> file is structured so that each row contains either a predicted toponym and an associated true toponym (along with assigned spatial coordinates), if the model correctly identified a toponym; otherwise the true toponym columns are empty for false positives and the predicted columns are empty for false negatives.</p> </li> </ul> <h3>Code</h3> <p>The code is split into two seperate folders <code>gpt_geoparser</code> and <code>notebooks</code>.</p> <ul> <li>**GPT_Geoparser: **this contains the classes and methods used process the XML and JSON articles (<code>data.py</code>), interact with the Nominatim API for geocoding (<code>gazetteer.py</code>), interact with the OpenAI API (<code>gpt_handler.py</code>), process the outputs from the GPT models (<code>geoparser.py</code>) and analyse the results (<code>analysis.py</code>).</li> <li><strong>Notebooks</strong>: This series of notebooks can be used to reproduce the results given in the paper. The file names a reasonably descriptive of what they do within the context of the paper.</li> </ul> <h3>Code/software</h3> <h3>Requirements</h3> <ul> <li>Numpy</li> <li>Pandas</li> <li>Geopy</li> <li>Scitkit-learn</li> <li>lxml</li> <li>openai</li> <li>matplotlib</li> <li>Contextily</li> <li>Shapely</li> <li>Geopandas</li> <li>tqdm</li> <li>huggingface_hub</li> <li>Gnews</li> </ul> <h3>Access information</h3> <p>Other publicly accessible locations of the data:</p> <ul> <li>The LGL and GeoVirus datasets can also be obtained <a href="https://github.com/milangritta/Pragmatic-Guide-to-Geoparsing-Evaluation" target="_blank" rel="noopener">here<span> (opens in new window)</span></a>.</li> </ul> <h3>Abstract</h3> <div> <p>Geoparsing- the process of associating textual data with geographic locations - is a key challenge in natural language processing. The often ambiguous and complex nature of geospatial language make geoparsing a difficult task, requiring sophisticated language modelling techniques. Recent developments in Large Language Models (LLMs) have demonstrated their impressive capability in natural language modelling, suggesting suitability to a wide range of complex linguistic tasks. In this paper, we evaluate the performance of four LLMs - GPT-3.5, GPT-4o, Llama-3.1-8b and Gemma-2-9b - in geographic information extraction by testing them on three geoparsing benchmark datasets: GeoVirus, LGL, and a novel dataset, News2024, composed of geotagged news articles published outside the models' training window. We demonstrate that, through techniques such as fine-tuning and retrieval-augmented generation, LLMs significantly outperform existing geoparsing models. The best performing models achieve a toponym extraction F1 score of 0.985 and toponym resolution accuracy within 161 km of 0.921. Additionally, we show that the spatial information encoded within the embedding space of these models may explain their strong performance in geographic information extraction. Finally, we discuss the spatial biases inherent in the models' predictions and emphasize the need for caution when applying these techniques in certain contexts.</p> </div> <h3>Methods</h3> <div> <p>This contains the data and codes required to reproduce the results from our paper. The LGL and GeoVirus datasets are pre-existing datasets, with references given in the manuscript. The News2024 dataset was constructed specifically for the paper. </p> <p>To construct the News2024 dataset, we first created a list of 50 cities from around the world which have population greater than 1000000. We then used the GNews python package <a href="https://pypi.org/project/gnews/" target="_blank" rel="noopener">https://pypi.org/project/gnews/<span> (opens in new window)</span></a> to find a news article for each location, published between 2024-05-01 and 2024-06-30 (inclusive). Of these articles, 47 were found to contain toponyms, with the three rejected articles referring to businesses which share a name with a city, and which did not otherwise mention any place names.</p> <p>We used a semi autonmous approach to geotagging the articles. The articles were first processed using a Distil-BERT model, fine tuned for named entity recognicion. This provided a first estimate of the toponyms within the text. A human reviewer then read the articles, and accepted or rejected the machine tags, and added any tags missing from the machine tagging process. We then used OpenStreetMap to obtain geographic coordinates for the location, and to identify the toponym type (e.g. city, town, village, river etc). We also flagged if the toponym was acting as a geo-political entity, as these were reomved from the analysis process. In total, 534 toponyms were identified in the 47 news articles. </p> </div> </div>
Influence of Family Language Policies on Language Proficiency across Generations: A Study of Russian-Speaking Families in Germany
Open the record for dataset details and reuse information.
Training datasets for "Multi-purpose controllable protein generation via prompted language models"
<div> <p>The datasets used to tune modular prompts of PROPEND fall into three main categories based on their design objectives: tertiary structure, secondary structure, and functional annotation.</p> </div>
Automating pharmacovigilance evidence generation: Using large language models to produce context-aware SQL
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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>
Supplementary Material, Towards the LLM-Based Generation of Formal Specifications from Natural-Language Contracts: Early Experiments with Symboleo
<p>This repository contains all the files used in the experiments described in the paper "Towards the LLM-Based Generation of Formal Specifications from Natural-Language Contracts: Early Experiments with Symboleo", which appeared in "RAISE 2025: Requirements engineering for AI-powered SoftwarE", an ICSE 2025 workshop, Ottawa, May 3, 2025.</p>
Supplementary materials of the paper entitled: "Generating Natural Language Requirements via Adversarial Examples in Deep Learning"
<p>Supplementary materials of the paper entitled: "Generating Natural Language Requirements via Adversarial Examples in Deep Learning"</p> <p>File A: Datasets (.txt)</p> <p> A1: Webex</p> <p> A2: Zoom</p> <p> A3: Teams</p> <p> A4: Word</p> <p> A5: PowerPoint</p> <p> A6: Excel</p> <p> </p> <p>File B: Python Code (Both ours and baseline)</p> <p> B1: adversarial_samples.ipynb</p> <p> B2: Baseline.ipynb</p> <p> </p> <p>FIle C: Result Tables (.xlsx)</p> <p> C1: Table of perturbed outputs in Webex </p> <p> C2: Table of perturbed outputs in Zoom</p> <p> C3: Table of perturbed outputs in Teams</p> <p> C4: Table of perturbed outputs in Word</p> <p> C5: Table of perturbed outputs in PowerPoint</p> <p> C6: Table of perturbed outputs in Excel (Excel)</p> <p> </p> <p>File D: Trend of Adversarial Shifts (Graphs)</p> <p> D1: Adversarial shifts of office suit (LSTM)</p> <p> D2: Adversarial shifts of video conferencing suit (LSTM)</p> <p> D3: Non-Adversarial shifts of office suit (LSTM)</p> <p> D4: Non-Adversarial shifts of video conferencing suit(LSTM)</p> <p> D5: Adversarial shifts of office suit (GRU)</p> <p> D6: Adversarial shifts of video conferencing suit (GRU)</p> <p> D7: Non-Adversarial shifts of office suit (GRU)</p> <p> D8: Non-Adversarial shifts of video conferencing suit(GRU)</p> <p> D9: Adversarial shifts of office suit (Bi-LSTM)</p> <p> D10: Adversarial shifts of video conferencing suit (Bi-LSTM)</p> <p> D11: Non-Adversarial shifts of office suit (Bi-LSTM)</p> <p> D12: Non-Adversarial shifts of video conferencing suit(Bi-LSTM)</p> <p> </p> <p>File E: Adversarial Examples (.pdf)</p> <p> E1: Adversarial vs original in Webex</p> <p> E2: Adversarial vs original in Zoom</p> <p> E3: Adversarial vs original in Teams</p> <p> E4: Adversarial vs original in Word</p> <p> E5: Adversarial vs original in PowerPoint</p> <p> E6: Adversarial vs original in Excel</p> <p> </p> <p>File F: Questionnaire</p> <p> </p>
Exploring The Potential of GPT-3-based Large Language Model For Melody Generation
<p>Here, we provide the dataset used, all generated melodies and melodies used to conduct subjective listening test.</p>
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>
Datasets for "Exploring the potential of neural machine translation for cross-language clinical NLP resource generation through annotation projection"
<p>This repository contains the data and additional resources used for the paper:</p> <p>"Exploring the Potential of Neural Machine Translation for Cross-Language Clinical NLP Resource Generation through Annotation Projection. Rodriguez Miret et al. Information (2024)".</p> <p>There are four different datasets included, namely:</p> <ul> <li>The (1) <strong><a href="https://temu.bsc.es/distemist" target="_blank" rel="noopener">DisTEMIST</a>, </strong>(2) <strong> <a href="https://temu.bsc.es/multicardioner" target="_blank" rel="noopener">DrugTEMIST</a> </strong>and<strong> </strong>(3) <strong><a href="https://temu.bsc.es/meddoprof" target="_blank" rel="noopener">MEDDOPROF</a> Spanish corpora and corresponding versions in 10 different languages</strong>, created through Machine Translation and annotation projection techniques. The Catalan annotations, used in the paper's experiments, were validated by bilingual expert annotators, who also provided alternative translations for the annotated terms in case they were wrongly translated. Thus, for Catalan we provide two different versions of the data: (i) the output of the annotation projection process as is, without any further validation, and (ii) the validated version of the data. For the rest of the languages (with the exception of the DrugTEMIST English and Italian data, used for the MultiCardioNER shared task), only an unvalidated version is provided.<strong><br></strong></li> <li>The (4) <strong>Catalan Clinical Case Corpus (CataCCC)</strong>, a collection of <em>200 clinical case reports in originally written in Catalan </em>covering a variety of clinical specialties. This corpus includes manually validated annotations for diseases, medications and professions created by the experts who annotated the corpora mentioned above, using the same guidelines and annottaion criteria. It can this be considered the first clinical Gold Standard corpus for diseases, medications and processions in Catalan.</li> </ul> <p>It is noteworthy that the MEDDOPROF-related data includes annotations for two labels, PROFESION and SITUACION_LABORAL, but only the former was used for training and evaluation in the paper.</p> <p>These are the <strong>10 languages</strong> included in the repository (along with their language codes):</p> <ul> <li><strong>Spanish</strong> (`es-gs`, with `gs` standing for Gold Standard)</li> <li><strong>Catalan</strong> (`cat`)</li> <li><strong>English</strong> (`en`)</li> <li><strong>French</strong> (`fr`)</li> <li><strong>Italian</strong> (`it`)</li> <li><strong>Dutch</strong> (`nl`)</li> <li><strong>Portuguese</strong> (`pt`)</li> <li><strong>Romanian</strong> (`ro`)</li> <li><strong>Swedish</strong> (`sv`)</li> <li><strong>Czech</strong> (`cz`)</li> </ul> <h2><strong>Related Links</strong></h2> <ul> <li><a href="../doi/10.5281/zenodo.13151039" target="_blank" rel="noopener">Validation and Correction Guidelines for the Multilingual Annotation Projection of Gold Standard Corpora</a></li> <li><a href="https://temu.bsc.es/multicardioner/" target="_blank" rel="noopener">MultiCardioNER Shared Task</a> </li> </ul> <h2><strong>License</strong></h2> <p>This work is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <h2><strong>Contact</strong></h2> <p>If you have any questions or suggestions, please contact us at:</p> <p>- Salvador Lima-López (<salvador [dot] limalopez [at] gmail [dot] com>)<br>- Martin Krallinger (<krallinger [dot] martin [at] gmail [dot] com>)</p>
The comparison of structured abstracts generated by the ChatGPT language model with the author's original abstracts derived from research papers
<p>The study utilized publications in the field of information science, both in Polish and English, which appeared in the journal <em>Zagadnienia Informacji Naukowej – Studia Informacyjne</em> during the 2022-2023 period. A total of 10 research papers were selected – 5 in Polish (PL1-PL5) and 5 in English (EN1-EN5).</p>
Artifacts for paper "CITYWALK: Enhancing LLM-Based C++ Unit Test Generation via Project-Dependency Awareness and Language-Specific Knowledge" submitted to TOSEM
<p>The project includes the data and code used in the submitted TOSEM paper titled "CITYWALK: Enhancing LLM-Based C++ Unit Test Generation via Project-Dependency Awareness and Language-Specific Knowledge"</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.