Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval
원문 초록
Retrieving relevant tables containing the necessary information to accurately answer a given question over tables is critical to open-domain question-answering (QA) systems. Previous methods assume the answer to such a question can be found either in a single table or multiple tables identified through question decomposition or rewriting. However, neither of these approaches is sufficient, as many questions require retrieving multiple tables and joining them through a join plan that cannot be discerned from the user query itself. If the join plan is not considered in the retrieval stage, the subsequent steps of reasoning and answering based on those retrieved tables are likely to be incorrect. To address this problem, we introduce a method that uncovers useful join relations for any query and database during table retrieval. We use a novel re-ranking method formulated as a mixed-integer program that considers not only table-query relevance but also table-table relevance that requires inferring join relationships. Our method outperforms the state-of-the-art approaches for table retrieval by up to 9.3% in F1 score and for end-to-end QA by up to 5.4% in accuracy.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
SimpleDoc: Multi-Modal Document Understanding with Dual-Cue Page Retrieval and Iterative Refinement
TableRAG: A Retrieval Augmented Generation Framework for Heterogeneous Document Reasoning
Efficient Context Selection for Long-Context QA: No Tuning, No Iteration, Just Adaptive-$k$
Answer Complex Questions: Path Ranker Is All You Need
From Roots to Rewards: Dynamic Tree Reasoning with RL
Response Quality Assessment for Retrieval-Augmented Generation via Conditional Conformal Factuality
토론
이 논문과 연결된 포럼 글이 아직 없습니다.