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---
license: mit
configs:
- config_name: meeting-qa
data_files:
- split: train
path: meeting/train.jsonl
- split: validation
path: meeting/dev.jsonl
- split: test
path: meeting/test.jsonl
- config_name: story-qa
data_files:
- split: train
path: story/train.jsonl
- split: validation
path: story/dev.jsonl
- split: test
path: story/test.jsonl
- config_name: meeting-corpus
data_files:
- split: corpus
path: meeting/corpus.jsonl
- config_name: story-corpus
data_files:
- split: corpus
path: story/corpus.jsonl
---
# MSRS: Evaluating Multi-Source Retrieval-Augmented Generation
**[📄 Paper](https://arxiv.org/abs/2508.20867) | [💻 Code](https://github.com/yale-nlp/MSRS)**
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:
```py
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:
```js
{
"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`):
```py
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:
```js
{
"id": // Unique ID for the query
"query": // Query text
"gold_documents": // List of gold document IDs
"answer": // List of answer summaries
}
``` |