TopClustRAG at SIGIR 2025 LiveRAG Challenge

• URL: http://arxiv.org/abs/2506.15246v1
• Date: Wed, 18 Jun 2025 08:24:27 GMT
• Title: TopClustRAG at SIGIR 2025 LiveRAG Challenge
• Authors: Juli Bakagianni, John Pavlopoulos, Aristidis Likas,
• Abstract summary: TopClustRAG is a retrieval-augmented generation (RAG) system developed for the LiveRAG Challenge.<n>Our system employs a hybrid retrieval strategy combining sparse and dense indices, followed by K-Means clustering to group semantically similar passages.
• Score: 2.56711111236449
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We present TopClustRAG, a retrieval-augmented generation (RAG) system developed for the LiveRAG Challenge, which evaluates end-to-end question answering over large-scale web corpora. Our system employs a hybrid retrieval strategy combining sparse and dense indices, followed by K-Means clustering to group semantically similar passages. Representative passages from each cluster are used to construct cluster-specific prompts for a large language model (LLM), generating intermediate answers that are filtered, reranked, and finally synthesized into a single, comprehensive response. This multi-stage pipeline enhances answer diversity, relevance, and faithfulness to retrieved evidence. Evaluated on the FineWeb Sample-10BT dataset, TopClustRAG ranked 2nd in faithfulness and 7th in correctness on the official leaderboard, demonstrating the effectiveness of clustering-based context filtering and prompt aggregation in large-scale RAG systems.

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