DyRRen: A Dynamic Retriever-Reranker-Generator Model for Numerical Reasoning over Tabular and Textual Data
This addresses the challenge of static retrieval in numerical reasoning for AI systems, though it appears incremental as it builds on existing retriever-generator pipelines.
The paper tackles the problem of numerical reasoning over hybrid tabular and textual data by proposing DyRRen, a dynamic retriever-reranker-generator model that dynamically reranks retrieved sentences at each generation step, and it outperforms existing baselines on the FinQA dataset.
Numerical reasoning over hybrid data containing tables and long texts has recently received research attention from the AI community. To generate an executable reasoning program consisting of math and table operations to answer a question, state-of-the-art methods use a retriever-generator pipeline. However, their retrieval results are static, while different generation steps may rely on different sentences. To attend to the retrieved information that is relevant to each generation step, in this paper, we propose DyRRen, an extended retriever-reranker-generator framework where each generation step is enhanced by a dynamic reranking of retrieved sentences. It outperforms existing baselines on the FinQA dataset.