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Acquired Podcast RAG Evaluation Dataset
Entertainment & Media Consumption
Tags and Keywords
Trusted By
"No reviews yet"
Free
About
This dataset provides a collection of Acquired Podcast Transcripts, specifically curated for evaluating Retrieval-Augmented Generation (RAG) systems. It includes human-verified answers and AI model responses both with and without access to the transcripts, along with correctness ratings and quality assessments. The dataset's core purpose is to facilitate the development and testing of AI models, particularly in the domain of natural language processing and question-answering.
Columns
The dataset contains several key columns designed for RAG evaluation:
- question: The query posed for evaluation.
- human_answer: The reference answer provided by a human.
- ai_answer_without_the_transcript: The answer generated by an AI model when it does not have access to the transcript.
- ai_answer_without_the_transcript_correctness: A human-verified assessment of the factual accuracy of the AI answer without the transcript (e.g., CORRECT, INCORRECT, Other).
- ai_answer_with_the_transcript: The answer generated by an AI model when it does have access to the transcript.
- ai_answer_with_the_transcript_correctness: A human-verified assessment of the factual accuracy of the AI answer with the transcript (e.g., CORRECT, INCORRECT, Other).
- quality_rating_for_answer_with_transcript: A human rating of the quality of the AI answer when the model had access to the transcript.
- post_url: The URL of the specific Acquired Podcast episode related to the question.
- file_name: The name of the transcript file corresponding to the episode.
Distribution
The dataset comprises 200 Acquired Podcast Transcripts, totalling approximately 3.5 million words. This is roughly equivalent to 5,500 pages when formatted into a Word document. It also includes a dedicated QA dataset for RAG evaluation, structured as a CSV file.
Usage
This dataset is ideal for:
- Evaluating the factual accuracy and quality of AI models, particularly those employing RAG techniques.
- Developing and refining natural language processing (NLP) models.
- Training and testing question-answering systems.
- Benchmarking the performance of different AI models in information retrieval tasks.
- Conducting research in artificial intelligence and machine learning, focusing on generative AI.
Coverage
The dataset's content is derived from 200 episodes of the Acquired Podcast, collected from its official website. It covers a range of topics typically discussed on the podcast, including business, technology, and finance. The data collection focused on transcripts available at the time of sourcing.
License
CC0
Who Can Use It
- AI/ML Researchers: For developing and testing new RAG models and NLP techniques.
- Data Scientists: For analysing and extracting insights from large text datasets and evaluating model performance.
- NLP Developers: For building and improving question-answering systems and conversational AI.
- Students and Academics: For educational projects and academic research in generative AI and data analytics.
- Data Providers: To understand best practices for creating and structuring evaluation datasets.
Dataset Name Suggestions
- Acquired Podcast RAG Evaluation Dataset
- Podcast Transcripts for AI QA
- Acquired QA Dataset for Generative AI
- Podcast RAG Performance Benchmark
- Acquired Transcripts & QA Evaluation
Attributes
Original Data Source: Acquired Podcast Transcripts and RAG Evaluation
QUALITY