Omkar2118/datasets huggingface dataset:
PrOntoQA
Full name: Proof and Ontology-Generated Question-Answering

**PrOntoQA** is a question-answering dataset which generates examples with chains-of-thought that describe the reasoning required to answer the questions correctly. The sentences in the examples are syntactically simple and amenable to semantic parsing. It can be used to formally analyze the predicted chain-of-thought from large language models such as GPT-3.

Source: [Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought](/paper/language-models-are-greedy-reasoners-a)

Image Source: [https://arxiv.org/pdf/2210.01240v1.pdf](https://arxiv.org/pdf/2210.01240v1.pdf)

Introducing paper: Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought
V-MIND

**V-MIND** enhanced the MIND dataset with news pictures.

Introducing paper: VLSNR:Vision-Linguistics Coordination Time Sequence-aware News Recommendation