# The Viability of Crowdsourcing for RAG Evaluation

arXiv: https://arxiv.org/abs/2504.15689 · HTML: https://mlsanity.com/paper/2504.15689/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 32 of its 53 references that are in the map.
- First posted on arXiv 22 Apr 2025.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 30 of 32)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods](https://mlsanity.com/paper/2412.05579/) (2024)
- [Benchmarking Large Language Models for News Summarization](https://mlsanity.com/paper/2301.13848/) (2023)
- [News Summarization and Evaluation in the Era of GPT-3](https://mlsanity.com/paper/2209.12356/) (2022)
- [Benchmarking Large Language Models in Retrieval-Augmented Generation](https://mlsanity.com/paper/2309.01431/) (2023)
- [All That's 'Human' Is Not Gold: Evaluating Human Evaluation of Generated Text](https://mlsanity.com/paper/2107.00061/) (2021)
- [ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2311.09476/) (2023)
- [MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries](https://mlsanity.com/paper/2401.15391/) (2024)
- [Large language models can accurately predict searcher preferences](https://mlsanity.com/paper/2309.10621/) (2023)
- [RAGBench: Explainable Benchmark for Retrieval-Augmented Generation Systems](https://mlsanity.com/paper/2407.11005/) (2024)
- [Perspectives on Large Language Models for Relevance Judgment](https://mlsanity.com/paper/2304.09161/) (2023)
- [RankME: Reliable Human Ratings for Natural Language Generation](https://mlsanity.com/paper/1803.05928/) (2018)
- [HAGRID: A Human-LLM Collaborative Dataset for Generative Information-Seeking with Attribution](https://mlsanity.com/paper/2307.16883/) (2023)
- [RECALL: A Benchmark for LLMs Robustness against External Counterfactual Knowledge](https://mlsanity.com/paper/2311.08147/) (2023)
- [Ragnar"ok: A Reusable RAG Framework and Baselines for TREC 2024 Retrieval-Augmented Generation Track](https://mlsanity.com/paper/2406.16828/) (2024)
- [One-Shot Labeling for Automatic Relevance Estimation](https://mlsanity.com/paper/2302.11266/) (2023)
- [Initial Nugget Evaluation Results for the TREC 2024 RAG Track with the AutoNuggetizer Framework](https://mlsanity.com/paper/2411.09607/) (2024)
- [LLM-based relevance assessment still can't replace human relevance assessment](https://mlsanity.com/paper/2412.17156/) (2024)
- [Don't Use LLMs to Make Relevance Judgments](https://mlsanity.com/paper/2409.15133/) (2024)
- [A Large-Scale Study of Relevance Assessments with Large Language Models: An Initial Look](https://mlsanity.com/paper/2411.08275/) (2024)
- [Towards Fine-Grained Citation Evaluation in Generated Text: A Comparative Analysis of Faithfulness Metrics](https://mlsanity.com/paper/2406.15264/) (2024)
- [DomainRAG: A Chinese Benchmark for Evaluating Domain-specific Retrieval-Augmented Generation](https://mlsanity.com/paper/2406.05654/) (2024)
- [LLMs Can Patch Up Missing Relevance Judgments in Evaluation](https://mlsanity.com/paper/2405.04727/) (2024)
- [Evaluating Generative Ad Hoc Information Retrieval](https://mlsanity.com/paper/2311.04694/) (2023)
- [A Workbench for Autograding Retrieve/Generate Systems](https://mlsanity.com/paper/2405.13177/) (2024)
- [SynDL: A Large-Scale Synthetic Test Collection for Passage Retrieval](https://mlsanity.com/paper/2408.16312/) (2024)
- [JudgeBlender: Ensembling Judgments for Automatic Relevance Assessment](https://mlsanity.com/paper/2412.13268/) (2024)

## Built on by (most cited first, 2 of 2)

- [Towards Transparent RAG: Fostering Evidence Traceability in LLM Generation via Reinforcement Learning](https://mlsanity.com/paper/2505.13258/) (2025)
- [AHEAD: Advancing Multi-Class Label Aggregation with Interpretable Cross-Annotator Modeling](https://mlsanity.com/paper/2607.18465/) (2026)

## Abstract (arXiv)

How good are humans at writing and judging responses in retrieval-augmented generation (RAG) scenarios? To answer this question, we investigate the efficacy of crowdsourcing for RAG through two complementary studies: response writing and response utility judgment. We present the Crowd RAG Corpus 2025 (CrowdRAG-25), which consists of 903 human-written and 903 LLM-generated responses for the 301 topics of the TREC RAG'24 track, across the three discourse styles 'bulleted list', 'essay', and 'news'. For a selection of 65 topics, the corpus further contains 47,320 pairwise human judgments and 10,556 pairwise LLM judgments across seven utility dimensions (e.g., coverage and coherence). Our analyses give insights into human writing behavior for RAG and the viability of crowdsourcing for RAG evaluation. Human pairwise judgments provide reliable and cost-effective results compared to LLM-based pairwise or human/LLM-based pointwise judgments, as well as automated comparisons with human-written reference responses. All our data and tools are freely available.