RMIT-ADM+S at the SIGIR 2025 LiveRAG Challenge -- GRAG: Generation-Retrieval-Augmented Generation
Abstract
This paper presents the RMIT-ADM+S participation in the SIGIR 2025 LiveRAG Challenge. Our Generation-Retrieval-Augmented Generation (GRAG) approach relies on generating a hypothetical answer that is used in the retrieval phase, alongside the original question. GRAG also incorporates a pointwise large language model (LLM)-based re-ranking step prior to final answer generation. We describe the system architecture and the rationale behind our design choices. In particular, a systematic evaluation using the Grid of Points (GoP) framework and N-way ANOVA enabled comparison across multiple configurations, including query variant generation, question decomposition, rank fusion strategies, and prompting techniques for answer generation. The submitted system achieved the highest Borda score based on the aggregation of Coverage, Relatedness, and Quality scores from manual evaluations, ranking first in the SIGIR 2025 LiveRAG Challenge.
