Simple/Complex Single-turn Prompts Dataset
Description
The dataset consists of 600 text-only prompts, each representing a fine-tuned instance of a single-turn user exchange in English. The samples are categorized into 10 distinct classes and cover 19 specific use cases. The dataset has been generated using ethically sourced human-in-the-loop data generation methods involving detailed insights of subject matter experts on labeled data for supervised fine-tuning to map input text with corresponding output responses.
The dataset is beneficial for direct preference optimization to generate responses that reinforce learning through human feedback. These techniques have been applied to align the fine-tuned conversational prompts with the desired output characteristics to ensure coherence, relevance, and alignment with the specified use cases and categories.
Key Features
- User Intent-Centric Prompts: Prompts are designed primarily to capture user intent and are formulated using natural language processing techniques.
- Conversational Interactions: The dataset facilitates interactive dialogues addressing a diverse range of queries in areas such as writing assistance, coding support, knowledge retrieval, data manipulation, logical reasoning, and classification tasks.
Dataset Source
Subject matter expert annotators @SoftAgeAI have annotated the data at simple and complex levels, focusing on quality factors such as content accuracy, clarity, coherence, grammar, depth of information, and overall usefulness.
Structure & Fields
The dataset is organized into five columns, which are detailed below:
S No (int64): A sequential identifier for each prompt, ranging from 1 to 600.
Prompts (object): The text of the prompt or query, which is the input given by the user. These prompts cover a wide range of topics, including shopping assistance, creative writing, Q&A, and more.
Use-cases (object): Describes the primary use case or application of the prompt. This categorization includes roles such as "Shopping assistant," "Creative writing assistant," "Q&A helper," and "Specialized knowledge helper."
Type (object): Indicates the complexity or nature of the prompt, with all entries in this dataset labeled as "Simple."
Categories (object): Provides a broader categorization of the prompt, such as "Open ended QA" or "Writing," offering additional context on the expected interaction or outcome.
Intended Use Cases
- The dataset is designed to improve the functionality of query assistance models across various domains, including coding, creative writing, travel support, marketing recommendations, citation management, academic writing, language translation, logical reasoning, research assistance, specialized knowledge-related, and STEM-related applications.
- The dataset aims to facilitate the development of generative models in fields such as e-commerce, customer support, educational applications, user query suggestions, and general-purpose chatbots.
- It is suitable for pre-training large language models utilizing supervised, fine-tuned annotated data and retrieval-augmented generative models.
- The dataset is curated to exclude interactions involving violence, harm, conflict, discrimination, brutality, and misinformation to ensure ethical use in its intended applications.
Potential Limitations & Biases
This is a static dataset, so the information is dated May 2024.
Note
If you have any questions related to our data annotation and human review services for large language model training and fine-tuning, please contact us at SoftAge Information Technology Limited at [email protected].
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