namespace-Pt/long-llm-data dataset hosted on Hugging Face and contributed by the HF Datasets community

Comprehensive dataset of Telegram users' geolocations with IP addresses, fully consented, comprising 50,000 records. Ideal for AI, ML, DL, and LLM training, this dataset provides detailed geospatial insights across various regions, enhancing geofencing, localization, and behavioral analysis models.

BackgroundClinical data is instrumental to medical research, machine learning (ML) model development, and advancing surgical care, but access is often constrained by privacy regulations and missing data. Synthetic data offers a promising solution to preserve privacy while enabling broader data access. Recent advances in large language models (LLMs) provide an opportunity to generate synthetic data with reduced reliance on domain expertise, computational resources, and pre-training.ObjectiveThis study aims to assess the feasibility of generating realistic tabular clinical data with OpenAI’s GPT-4o using zero-shot prompting, and evaluate the fidelity of LLM-generated data by comparing its statistical properties to the Vital Signs DataBase (VitalDB), a real-world open-source perioperative dataset.MethodsIn Phase 1, GPT-4o was prompted to generate a dataset with qualitative descriptions of 13 clinical parameters. The resultant data was assessed for general errors, plausibility of outputs, and cross-verification of related parameters. In Phase 2, GPT-4o was prompted to generate a dataset using descriptive statistics of the VitalDB dataset. Fidelity was assessed using two-sample t-tests, two-sample proportion tests, and 95% confidence interval (CI) overlap.ResultsIn Phase 1, GPT-4o generated a complete and structured dataset comprising 6,166 case files. The dataset was plausible in range and correctly calculated body mass index for all case files based on respective heights and weights. Statistical comparison between the LLM-generated datasets and VitalDB revealed that Phase 2 data achieved significant fidelity. Phase 2 data demonstrated statistical similarity in 12/13 (92.31%) parameters, whereby no statistically significant differences were observed in 6/6 (100.0%) categorical/binary and 6/7 (85.71%) continuous parameters. Overlap of 95% CIs were observed in 6/7 (85.71%) continuous parameters.ConclusionZero-shot prompting with GPT-4o can generate realistic tabular synthetic datasets, which can replicate key statistical properties of real-world perioperative data. This study highlights the potential of LLMs as a novel and accessible modality for synthetic data generation, which may address critical barriers in clinical data access and eliminate the need for technical expertise, extensive computational resources, and pre-training. Further research is warranted to enhance fidelity and investigate the use of LLMs to amplify and augment datasets, preserve multivariate relationships, and train robust ML models.

Dataset

This dataset was created by Hemanthh Velliyangirie

Released under Apache 2.0

Contents

This Private Company Data dataset is a refined version of our company datasets, consisting of 35M+ data records.

It’s an excellent data solution for companies with limited data engineering capabilities and those who want to reduce their time to value. You get filtered, cleaned, unified, and standardized B2B private company data. This data is also enriched by leveraging a carefully instructed large language model (LLM).

AI-powered data enrichment offers more accurate information in key data fields, such as company descriptions. It also produces over 20 additional data points that are very valuable to B2B businesses. Enhancing and highlighting the most important information in web data contributes to quicker time to value, making data processing much faster and easier.

For your convenience, you can choose from multiple data formats (Parquet, JSON, JSONL, or CSV) and select suitable delivery frequency (quarterly, monthly, or weekly).

Coresignal is a leading private company data provider in the web data sphere with an extensive focus on firmographic data and public employee profiles. More than 3B data records in different categories enable companies to build data-driven products and generate actionable insights. Coresignal is exceptional in terms of data freshness, with 890M+ records updated monthly for unprecedented accuracy and relevance.

Top artificial intelligence firms are racing to build the biggest and most powerful Nvidia server chip clusters to win in AI. Below, we mapped the biggest completed and planned server clusters. Check back often, as we'll update the list when we confirm more data.

Dataset Card for "llm-sgd-dst8-split-training-data"

More Information needed

Dataset

This dataset was created by kami

Released under CC0: Public Domain

Contents

Dataset

This dataset was created by JISU KIM8873

Contents

Large Language Models (LLMs) offer new research possibilities for social scientists, but their potential as “synthetic data" is still largely unknown. In this paper, we investigate how accurately the popular LLM ChatGPT can recover public opinion, prompting the LLM to adopt different “personas” and then provide feeling thermometer scores for 11 sociopolitical groups. The average scores generated by ChatGPT correspond closely to the averages in our baseline survey, the 2016–2020 American National Election Study. Nevertheless, sampling by ChatGPT is not reliable for statistical inference: there is less variation in responses than in the real surveys, and regression coefficients often differ significantly from equivalent estimates obtained using ANES data. We also document how the distribution of synthetic responses varies with minor changes in prompt wording, and we show how the same prompt yields significantly different results over a three-month period. Altogether, our findings raise serious concerns about the quality, reliability, and reproducibility of synthetic survey data generated by LLMs.

chengpingan/LLM-QE-DPO-Training-Data dataset hosted on Hugging Face and contributed by the HF Datasets community

LLM-Based Vulnerability Classification in Police Narratives This repository contains datasets used in our research on applying large language models (LLMs) to identify indicators of vulnerability in police incident narratives. These resources support the replication of findings in our paper: "Using Instruction-Tuned Large Language Models to Identify Indicators of Vulnerability in Police Incident Narratives."

Project Overview Law enforcement frequently encounters vulnerable individuals, but identifying vulnerability factors in police records remains challenging. Our research explores how LLMs can assist in identifying four key vulnerability indicators in police Field Interrogation and Observation (FIO) narratives:

Mental health issues Drug abuse Alcoholism Homelessness

This project advances police research methodology by: 1. Evaluating LLM performance in vulnerability classification against human labelers 2. Comparing different LLM architectures and prompt engineering approaches 3. Investigating potential demographic biases through counterfactual analysis 4. Developing a reusable framework for qualitative text analysis

Datasets This repository includes four key datasets:

boston_narratives_test_classified_4000.csv: 4,000 narratives classified with our LLM pipeline, including all labels and model explanations counterfactual_narratives_all_coded.csv: Systematically generated counterfactual narratives with varied demographic characteristics examples_for_counterfactuals.csv: 100 base narratives used for counterfactual generation labelled_fio_data_for_analysis.csv: 500 pre-processed examples with human and GPT-4o labels

Code Repository The complete codebase for replicating our research is available in our GitHub repository: llm-deductive-coding (particularly in the boston_fio_paper directory).

The repository includes: - Data preprocessing scripts - Classification pipeline implementation - Counterfactual generation code - Analysis notebooks - Visualization tools

Citation If you use these resources in your research, please cite our paper:

bibtex @article{author2023llm, title={Using Instruction-Tuned Large Language Models to Identify Indicators of Vulnerability in Police Incident Narratives}, author={Relins, S. and Birks, D and Lloyd, C}, journal={Arxiv Preprint}, year={2023}, note={Currently under review for the Journal of Quantitative Criminology} }

License These datasets are released under the MIT License. The original Boston FIO data is released under the Open Data Commons Public Domain Dedication and License (PDDL).

Contact For questions about this research or datasets, please contact the authors or open an issue in our GitHub repository.

This data was collected in an experiment aiming to establish whether trust in large language models (LLMs) may be inflated in relation to other forms of artificial intelligence, with a particular focus on the content and forms of natural language used. One hundred and ninety-nine residents of the United States were recruited online and presented with a series of general knowledge questions. For each question they also received a recommendation from either an LLM or a non-LLM AI-assistant. The accuracy of this recommendation was also varied. All data is deidentified and there is no missing data. This deidentified data may be used by researchers for the purposes of verifying published results or advancing other research on this topic. Lineage: Data was collected on the Qualtrics survey platform from participants sourced on online recruitment platform, Prolific.

In this paper, we introduce a novel benchmarking framework designed specifically for evaluations of data science agents. Our contributions are three-fold. First, we propose DSEval, an evaluation paradigm that enlarges the evaluation scope to the full lifecycle of LLM-based data science agents. We also cover aspects including but not limited to the quality of the derived analytical solutions or machine learning models, as well as potential side effects such as unintentional changes to the original data. Second, we incorporate a novel bootstrapped annotation process letting LLM themselves generate and annotate the benchmarks with ``human in the loop''. A novel language (i.e., DSEAL) has been proposed and the derived four benchmarks have significantly improved the benchmark scalability and coverage, with largely reduced human labor. Third, based on DSEval and the four benchmarks, we conduct a comprehensive evaluation of various data science agents from different aspects. Our findings reveal the common challenges and limitations of the current works, providing useful insights and shedding light on future research on LLM-based data science agents.

This is one of DSEval benchmarks.

1rsh/singled-llm-evaluator-data dataset hosted on Hugging Face and contributed by the HF Datasets community

Advancing Homepage2Vec with LLM-Generated Datasets for Multilingual Website Classification

This dataset contains two subsets of labeled website data, specifically created to enhance the performance of Homepage2Vec, a multi-label model for website classification. The datasets were generated using Large Language Models (LLMs) to provide more accurate and diverse topic annotations for websites, addressing a limitation of existing Homepage2Vec training data.

Key Features:

LLM-generated annotations: Both datasets feature website topic labels generated using LLMs, a novel approach to creating high-quality training data for website classification models.

Improved multi-label classification: Fine-tuning Homepage2Vec with these datasets has been shown to improve its macro F1 score from 38% to 43% evaluated on a human-labeled dataset, demonstrating their effectiveness in capturing a broader range of website topics.

Multilingual applicability: The datasets facilitate classification of websites in multiple languages, reflecting the inherent multilingual nature of Homepage2Vec.

Dataset Composition:

curlie-gpt3.5-10k: 10,000 websites labeled using GPT-3.5, context 2 and 1-shot

curlie-gpt4-10k: 10,000 websites labeled using GPT-4, context 2 and zero-shot

Intended Use:

Fine-tuning and advancing Homepage2Vec or similar website classification models

Research on LLM-generated datasets for text classification tasks

Exploration of multilingual website classification

Additional Information:

Project and report repository: https://github.com/CS-433/ml-project-2-mlp

Acknowledgments:

This dataset was created as part of a project at EPFL's Data Science Lab (DLab) in collaboration with Prof. Robert West and Tiziano Piccardi.

What’s Included

Welcome to the Bahasa Open Ended Classification Prompt-Response Dataset—an extensive collection of 3000 meticulously curated prompt and response pairs. This dataset is a valuable resource for training Language Models (LMs) to classify input text accurately, a crucial aspect in advancing generative AI.

These prompt and completion pairs cover a broad range of topics, including science, history, technology, geography, literature, current affairs, and more. Each prompt is accompanied by a response, providing valuable information and insights to enhance the language model training process. Both the prompt and response were manually curated by native Bahasa people, and references were taken from diverse sources like books, news articles, websites, and other reliable references.

This open-ended classification prompt and completion dataset contains different types of prompts, including instruction type, continuation type, and in-context learning (zero-shot, few-shot) type. The dataset also contains prompts and responses with different types of rich text, including tables, code, JSON, etc., with proper markdown.

The Bahasa version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

Energy consumption of artificial intelligence (AI) models in training is considerable, with both GPT-3, the original release of the current iteration of OpenAI's popular ChatGPT, and Gopher consuming well over a thousand-megawatt hours of energy simply for training. As this is only for the training model it is likely that the energy consumption for the entire usage and lifetime of GPT-3 and other large language models (LLMs) is significantly higher. The largest consumer of energy, GPT-3, consumed roughly the equivalent of 200 Germans in 2022. While not a staggering amount, it is a considerable use of energy.

Energy savings through AI

While it is undoubtedly true that training LLMs takes a considerable amount of energy, the energy savings are also likely to be substantial. Any AI model that improves processes by minute numbers might save hours on shipment, liters of fuel, or dozens of computations. Each one of these uses energy as well and the sum of energy saved through a LLM might vastly outperform its energy cost. A good example is mobile phone operators, of which a third expect that AI might reduce power consumption by ten to fifteen percent. Considering that much of the world uses mobile phones this would be a considerable energy saver.

Emissions are considerable

The amount of CO2 emissions from training LLMs is also considerable, with GPT-3 producing nearly 500 tonnes of CO2. This again could be radically changed based on the types of energy production creating the emissions. Most data center operators for instance would prefer to have nuclear energy play a key role, a significantly low-emission energy producer.

IntroductionThe rise of accessible, consumer facing large language models (LLM) provides an opportunity for immediate diagnostic support for clinicians.ObjectivesTo compare the different performance characteristics of common LLMS utility in solving complex clinical cases and assess the utility of a novel tool to grade LLM output.MethodsUsing a newly developed rubric to assess the models’ diagnostic utility, we measured to models’ ability to answer cases according to accuracy, readability, clinical interpretability, and an assessment of safety. Here we present a comparative analysis of three LLM models—Bing, Chat GPT, and Gemini—across a diverse set of clinical cases as presented in the New England Journal of Medicines case series.ResultsOur results suggest that models performed differently when presented with identical clinical information, with Gemini performing best. Our grading tool had low interobserver variability and proved a reliable tool to grade LLM clinical output.ConclusionThis research underscores the variation in model performance in clinical scenarios and highlights the importance of considering diagnostic model performance in diverse clinical scenarios prior to deployment. Furthermore, we provide a new tool to assess LLM output.

This is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of instruction fine tuned LLMs. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for mitigating that trigger behavior in the trained AI models.