MSRS: Evaluating Multi-Source Retrieval-Augmented Generation
Paper • 2508.20867 • Published
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Bed008 | "grad a: Alright , so I 'm - I should read all of these numbers ? professor b: OK . professor b: OK (...TRUNCATED) |
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Bed010 | "grad b: grad d: How many batteries do you go through ? grad b: Thank you . My suggestion is that R(...TRUNCATED) |
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MSRS: Evaluating Multi-Source Retrieval-Augmented Generation
This paper introduces a scalable framework for constructing evaluation benchmarks that challenge RAG systems to integrate information across distinct sources and generate long-form responses. Using our framework, we build two new benchmarks on Multi-Source Retrieval and Synthesis: MSRS-Story and MSRS-Meet.
🚀 Quickstart
Load the corpora for MSRS-Story and MSRS-Meet:
from datasets import load_dataset
story_corpus = load_dataset("yale-nlp/MSRS", "story-corpus", split="corpus")
meeting_corpus = load_dataset("yale-nlp/MSRS", "meeting-corpus", split="corpus")
Corpus Dataset Example:
{
"id": // Unique ID for the document
"text": // Document text
}
Load the query-answer pairs for MSRS-Story and MSRS-Meet (available splits: train, test, and validation):
from datasets import load_dataset
story_qa = load_dataset("yale-nlp/MSRS", "story-qa")
meeting_qa = load_dataset("yale-nlp/MSRS", "meeting-qa")
QA Dataset Example:
{
"id": // Unique ID for the query
"query": // Query text
"gold_documents": // List of gold document IDs
"answer": // List of answer summaries
}
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