Prompts generated from ChatGPT3.5 and ChatGPT4 with NYT and HC3 topics in different roles and parameter configurations.

The dataset is useful to study lexical aspects of LLMs with different parameters/roles configurations.

• The 0_Base_Topics.xlsx file lists the topics used for the dataset generation
• The rest of the files collect the answers of ChatGPT to these topics with different configurations of parameters/context:
  • Temperature (parameter): Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.
  • Frequency penalty (parameter): Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.
  • Top probability (parameter): An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass.
  • Presence penalty (parameter): Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.
  • Roles (context)
    • Default: No role is assigned to the LLM, the default role is used.
    • Child: The LLM is requested to answer as a five-year-old child.
    • Young adult male: The LLM is requested to answer as a young male adult.
    • Young adult female: The LLM is requested to answer as a young female adult.
    • Elderly adult male: The LLM is requested to answer as an elderly male adult.
    • Elderly adult female: The LLM is requested to answer as an elderly female adult.
    • Affluent adult male: The LLM is requested to answer as an affluent male adult.
    • Affluent adult female: The LLM is requested to answer as an affluent female adult.
    • Lower-class adult male: The LLM is requested to answer as a lower-class male adult.
    • Lower-class adult female: The LLM is requested to answer as a lower-class female adult.
    • Erudite: The LLM is requested to answer as an erudite who uses a rich vocabulary.

The Sotheby's International Realty dataset provides a premium collection of real estate data, ideal for training AI models and enhancing various business operations in the luxury real estate market. Our data is carefully curated and prepared to ensure seamless integration with your AI systems, allowing you to innovate and optimize your business processes with minimal effort. This dataset is versatile and suitable for small boutique agencies, mid-sized firms, and large real estate enterprises.

Key features include:

Custom Delivery Options: Data can be delivered through Rest-API, Websockets, tRPC/gRPC, or other preferred methods, ensuring smooth integration with your AI infrastructure. Vectorized Data: Choose from multiple embedding models (LLama, ChatGPT, etc.) and vector databases (Chroma, FAISS, QdrantVectorStore) for optimal AI model performance and vectorized data processing. Comprehensive Data Coverage: Includes detailed property listings, luxury market trends, customer engagement data, and agent performance metrics, providing a robust foundation for AI-driven analytics. Ease of Integration: Our dataset is designed for easy integration with existing AI systems, providing the flexibility to create AI-driven analytics, notifications, and other business applications with minimal hassle. Additional Services: Beyond data provision, we offer AI agent development and integration services, helping you seamlessly incorporate AI into your business workflows. With this dataset, you can enhance property valuation models, optimize customer engagement strategies, and perform advanced market analysis using AI-driven insights. This dataset is perfect for training AI models that require high-quality, structured data, helping luxury real estate businesses stay competitive in a dynamic market.

Dataset Card for UltraChat 200k

  Dataset Description

This is a heavily filtered version of the UltraChat dataset and was used to train Zephyr-7B-β, a state of the art 7b chat model. The original datasets consists of 1.4M dialogues generated by ChatGPT and spanning a wide range of topics. To create UltraChat 200k, we applied the following logic:

Selection of a subset of data for faster supervised fine tuning. Truecasing of the dataset, as we observed around 5% of… See the full description on the dataset page: https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k.

As ChatGPT emerges as a potential ally in healthcare decision-making, it is imperative to investigate how users leverage and perceive it. The repurposing of technology is innovative but brings risks, especially since AI’s effectiveness depends on the data it’s fed. In healthcare, ChatGPT might provide sound advice based on current medical knowledge, which could turn into misinformation if its data sources later include erroneous information. Our study assesses user perceptions of ChatGPT, particularly of those who used ChatGPT for healthcare-related queries. By examining factors such as competence, reliability, transparency, trustworthiness, security, and persuasiveness of ChatGPT, the research aimed to understand how users rely on ChatGPT for health-related decision-making. A web-based survey was distributed to U.S. adults using ChatGPT at least once a month. Bayesian Linear Regression was used to understand how much ChatGPT aids in informed decision-making. This analysis was conducted on subsets of respondents, both those who used ChatGPT for healthcare decisions and those who did not. Qualitative data from open-ended questions were analyzed using content analysis, with thematic coding to extract public opinions on urban environmental policies. Six hundred and seven individuals responded to the survey. Respondents were distributed across 306 US cities of which 20 participants were from rural cities. Of all the respondents, 44 used ChatGPT for health-related queries and decision-making. In the healthcare context, the most effective model highlights ’Competent + Trustworthy + ChatGPT for healthcare queries’, underscoring the critical importance of perceived competence and trustworthiness specifically in the realm of healthcare applications of ChatGPT. On the other hand, the non-healthcare context reveals a broader spectrum of influential factors in its best model, which includes ’Trustworthy + Secure + Benefits outweigh risks + Satisfaction + Willing to take decisions + Intent to use + Persuasive’. In conclusion our study findings suggest a clear demarcation in user expectations and requirements from AI systems based on the context of their use. We advocate for a balanced approach where technological advancement and user readiness are harmonized.

In 2023, the tech skill most in demand by recruiters was web development. This was closely followed by DevOps and database software skills. Interestingly, over 16 percent of recruiters were actively seeking individuals with cybersecurity skills. Not far behind, AI/Machine learning/Deep learning ranked fourth, with approximately 25 percent of respondents identifying it as their most sought-after tech skill. These preferences align with the skills that developers worldwide are keen to acquire, particularly web development and AI/Machine learning/Deep learning.

AI at the forefront of IT skills Since the release of ChatGPT in late 2022, demand for AI and automation skills has increased across all sectors. In 2023, ChatGPT was the leading technology skill globally according to topic consumption on Udemy Business, experiencing a massive growth of over 4,400 percent in global topic consumption. In the same year, over 82 percent of software developers reported using AI to help write code in the development workflow, while another 49 percent said they currently use it for debugging code.

Different languages for different needs JavaScript and Java, commonly used for back-end and front-end web development, were the most demanded programming languages worldwide in 2022, followed by SQL and Python. By industry, JavaScript and Java hold the fort in the IT services and aviation industries, while SQL was more popular in the healthcare sector as well as the marketing and advertising industries. Python, well suited for data science applications, was more commonly used in the manufacturing, education, and energy industries.