HELIOS: Harmonizing Early Fusion, Late Fusion, and LLM Reasoning for Multi-Granular Table-Text Retrieval

Table-text retrieval aims to retrieve relevant tables and text to support open-domain question answering.Existing studies use either early or late fusion, but face limitations.Early fusion pre-aligns a table row with its associated passages, forming "stars," which often include irrelevant contexts and miss query-dependent relationships.Late fusion retrieves individual nodes, dynamically aligning them, but it risks missing relevant contexts.Both approaches also struggle with advanced reasoning tasks, such as column-wise aggregation and multihop reasoning.To address these issues, we propose HELIOS, which combines the strengths of both approaches.First, the edge-based bipartite subgraph retrieval identifies finer-grained edges between table segments and passages, effectively avoiding the inclusion of irrelevant contexts.Then, the query-relevant node expansion identifies the most promising nodes, dynamically retrieving relevant edges to grow the bipartite subgraph, minimizing the risk of missing important contexts.Lastly, the star-based LLM refinement performs logical inference at the star graph level rather than the bipartite subgraph, supporting advanced reasoning tasks.Experimental results show that HELIOS outperforms state-of-the-art models with a significant improvement up to 42.6% and 39.9% in recall and nDCG, respectively, on the OTT-QA benchmark.