gpt3
Datasets
All datasets matching “gpt3”2wikimultihopqa_with_q_gpt35
2WikiMultihopQA Dataset with GPT-3.5 Generated Questions
Overview
This repository hosts an enhanced version of the 2WikiMultihopQA dataset, where each supporting sentence in the dataset has been supplemented with questions generated using OpenAI's GPT-3.5 turbo API. The aim is to provide a richer context for each entry, potentially benefiting various NLP tasks, such as question answering and context understanding.
Dataset Format
Each entry in the dataset is… See the full description on the dataset page: https://huggingface.co/datasets/scholarly-shadows-syndicate/2wikimultihopqa_with_q_gpt35.wiki_bio_gpt3_hallucination
Dataset Card for WikiBio GPT-3 Hallucination Dataset
GitHub repository: https://github.com/potsawee/selfcheckgpt
Paper: SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
Dataset Summary
We generate Wikipedia-like passages using GPT-3 (text-davinci-003) using the prompt: This is a Wikipedia passage about {concept} where concept represents an individual from the WikiBio dataset.
We split the generated passages into… See the full description on the dataset page: https://huggingface.co/datasets/potsawee/wiki_bio_gpt3_hallucination.sst2hotpotqa_with_qa_gpt35
HotpotQA Dataset with GPT-3.5 Generated Questions
Overview
This repository hosts an enhanced version of the HotpotQA dataset, where each supporting sentence in the dataset has been supplemented with questions generated using OpenAI's GPT-3.5 turbo API. The aim is to provide a richer context for each entry, potentially benefiting various NLP tasks, such as question answering and context understanding.
Dataset Format
Each entry in the dataset is formatted as… See the full description on the dataset page: https://huggingface.co/datasets/scholarly-shadows-syndicate/hotpotqa_with_qa_gpt35.QuRating-GPT3.5-Judgments-Test7140 pairwise judgments across 4 criteria and 6 domains obtained by prompting GPT-3.5-turbo-0613 for evaluating QuRater models.
From the paper: QuRating: Selecting High-Quality Data for Training Language Models
Guidance on Responsible Use
In the paper, we document various types of bias that are present in the quality ratings/QuRater model (biases related to domains, topics, social roles, regions and languages - see Section 6 of the paper),
which are likely reflected in the LLM judgments.
Hence… See the full description on the dataset page: https://huggingface.co/datasets/princeton-nlp/QuRating-GPT3.5-Judgments-Test.prompt_injection_hackaprompt_gpt35
Dataset Card for "prompt_injection_hackaprompt_gpt35"
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