Abstract
Retrieval-Augmented Generation (RAG) enriches Large Language Models (LLMs) by combining their internal, parametric knowledge with external, non-parametric sources, with the goal of improving factual correctness and minimizing hallucinations. The LiveRAG 2025 challenge explores RAG solutions to maximize accuracy on DataMorgana's QA pairs, which are composed of single-hop and multi-hop questions. The challenge provides access to sparse OpenSearch and dense Pinecone indices of the Fineweb 10BT dataset. It restricts model use to LLMs with up to 10B parameters and final answer generation with Falcon-3-10B. A judge-LLM assesses the submitted answers along with human evaluators. By exploring distinct retriever combinations and RAG solutions under the challenge conditions, our final solution emerged using InstructRAG in combination with a Pinecone retriever and a BGE reranker. Our solution achieved a correctness score of 1.13 and a faithfulness score of 0.55, placing fourth in the SIGIR 2025 LiveRAG Challenge.
Abstract (translated)
检索增强生成(RAG)通过将大型语言模型(LLMs)的内部参数化知识与外部非参数化来源相结合,旨在提高事实准确性并最小化幻觉。LiveRAG 2025挑战赛探索了各种RAG解决方案,以最大限度地提高在DataMorgana问题回答对上的准确度,这些问题包括单跳和多跳问题。该挑战提供了访问Fineweb 10BT数据集的稀疏OpenSearch索引和密集Pinecone索引的机会,并限制使用最多具有10B参数的LLMs进行最终答案生成,且使用Falcon-3-10B模型来生成答案。由裁判LLM和人类评估员一起评审提交的答案。 通过在挑战条件下探索不同的检索器组合及RAG解决方案,我们的最终方案采用的是InstructRAG与Pinecone检索器和BGE重排序器的结合方式。我们的解决方案取得了1.13的确切性得分和0.55的忠实度得分,在SIGIR 2025 LiveRAG挑战中排名第四。
URL
https://arxiv.org/abs/2506.14412