LASAGNA评估检索增强生成（RAG）管道的组成性
Retrieval Augmented Generation Compositionality
LASAGNA评估了450种RAG管道配置在8个基准和5K-200K文档语料库上的表现，发现不同阶段单个技术优势不必然组合有效，检索质量与答案忠实度优化方向不同，最佳组合随语料库规模变化，表明孤立测试结果不可靠，新RAG技术需在完整环境中评估。
Published on Oct 21
Authors:
,
Abstract
LASAGNA evaluates composed RAG pipelines across scales and finds that isolated technique gains do not reliably predict combined retrieval quality, faithfulness, or large-corpus performance.
Retrieval Augmented Generation (RAG) grounds large language models in external knowledge. Modern pipelines combine embedding, chunking, retrieval, reranking, query augmentation, context compression, and agentic verification, yet these techniques are typically evaluated one at a time. It remains unclear whether their isolated gains survive composition, whether retrieval quality and answer faithfulness favor the same design choices, and whether conclusions from small corpora hold at large scale. We pre-declare three hypotheses and introduce LASAGNA, an open-source framework for controlled RAG evaluation. We test 450 configurations across eight benchmarks and corpora ranging from 5K to 200K documents. Three findings emerge. First, individually strong techniques from different phases of a RAG pipeline do not necessarily compose: three retrieval techniques predict a +47% combined improvement but deliver only +17% when used together. Second, component gains stack differently for retrieval and faithfulness, and optimizing one can hurt the other. Third, the best composition changes as the corpus grows: the combination that wins on a small corpus may not remain best at large scale. These findings show that gains measured in isolation are not reliable predictors of realistic performance, and that new RAG techniques should be tested with the full surrounding stack. We call on researchers to do so and supply LASAGNA to simplify these experiments.
View arXiv page View PDF Project page GitHub 1 Add to collection
Get this paper in your agent:
hf papers read 2610.29722
Don't have the latest CLI?
curl -LsSf https://hf.co/cli/install.sh | bash
Models citing this paper 0
No model linking this paper
Cite arxiv.org/abs/2610.29722 in a model README.md to link it from this page.
Datasets citing this paper 0
No dataset linking this paper
Cite arxiv.org/abs/2610.29722 in a dataset README.md to link it from this page.
Spaces citing this paper 0
No Space linking this paper
Cite arxiv.org/abs/2610.29722 in a Space README.md to link it from this page.
Collections including this paper 0
No Collection including this paper
Add this paper to a collection to link it from this page.
来源: Hugging Face Daily Papers · huggingface.co