Omkar2118/datasets huggingface dataset:
LCStep

For our experiments, we collected a dataset of procedural knowledge of the LangChain Python library, unseen by many extant LLMs. We selected LangChain as the domain for our dataset because it was published in 2022, which is later than the knowledge cutoff date for many web-scale LLMs, including GPT-3.5, while also having plenty of documentation due to its popularity.

The LCStep dataset was collected from 180 tutorial pages in the Python section of the LangChain website. We used an LLM-enabled pipeline with human oversight and quality review to extract 276 procedures from these tutorials, representing each procedure in a format structure.

Introducing paper: Pairing Analogy-Augmented Generation with Procedural Memory for Procedural Q&A
CHAMP
Full name: Concept and Hint-Annotated Math Problems

The Concept and Hint-Annotated Math Problems (CHAMP) consists of high school math competition problems, annotated with concepts, or general math facts, and hints, or problem-specific tricks. These annotations allow us to explore the effects of additional information, such as relevant hints, misleading concepts, or related problems.