Synthia-v1.3-Implicit-Reasoning

Original dataset creator:
https://huggingface.co/migtissera

This dataset is a refined subset of the Synthia-v1.3 dataset, consisting of 23,573 entries, designed to train AI models in reasoning without step-by-step or chain-of-thought prompting.
All system messages and reasoning prompts were edited or replaced with basic prompting to encourage models to naturally develop logical progression in responses. The dataset includes real-world scenarios and challenges that promote autonomous problem-solving across various topics, without explicitly guiding models toward reasoning. This format allows for training in a more natural conversational environment.

Dataset Composition

Size: 23,573 entries derived from the Synthia-v1.3 dataset
Format: Modified user queries and responses in a reasoning-free structure
Focus: Logical problem-solving without explicit reasoning instructions

Applications

Enhancing natural reasoning in AI models for conversational intelligence and educational tools.
AI model training for improved problem-solving and logical progression in responses.

Acknowledgment

Special thanks to Migel Tissera, PhD in Deep Learning and Co-founder & CTO of Metaspectral, for his outstanding work on the original dataset. For more details, visit the original Synthia-v1.3 dataset. https://huggingface.co/datasets/migtissera/Synthia-v1.3

原始数据集创建者：https://huggingface.co/migtissera

本数据集为Synthia-v1.3数据集的精筛子集，共包含23573条数据条目，旨在无需采用逐步提示或思维链（Chain-of-Thought）提示方式的前提下，训练AI模型的推理能力。所有系统消息与推理提示均已被编辑或替换为基础提示词，以促使模型在回复中自然形成逻辑推演过程。本数据集涵盖各类真实场景与挑战，可推动模型在无需明确推理引导的前提下，自主解决多主题领域的问题。该数据格式能够在更自然的对话环境中开展模型训练。

数据集构成
规模：源自Synthia-v1.3数据集，共23573条数据条目
格式：采用无推理提示结构的经修改用户查询与回复内容
核心定位：在无明确推理指令的前提下实现逻辑化问题求解

应用场景
面向对话智能与教育工具，提升AI模型的自然推理能力
用于AI模型训练，以优化其回复中的问题求解能力与逻辑推演过程

致谢声明
特别致谢Migel Tissera——深度学习博士、Metaspectral联合创始人兼首席技术官，感谢其在原始数据集开发中所作出的卓越贡献。如需了解更多细节，请访问原始Synthia-v1.3数据集页面：https://huggingface.co/datasets/migtissera/Synthia-v1.3