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arXiv · 2506.15246

TopClustRAG at SIGIR 2025 LiveRAG Challenge

Juli Bakagianni·John Pavlopoulos·Aristidis Likas
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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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Juli Bakagianni, John Pavlopoulos, Aristidis Likas. 2025-06-18. TopClustRAG at SIGIR 2025 LiveRAG Challenge. https://arxiv.org/abs/2506.15246

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