What is the impact of iterative retrieval agent depth (number of retrieval rounds) on final answer F1 score an

Abstract

Abstract: In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering. To aggregate clues from scattered texts across multiple paragraphs, a hierarchical graph is created by constructing nodes on different levels of granularity (questions, paragraphs, sentences, entities), the representations of which are initialized with pre-trained contextual encoders. Given this hierarchical graph, the initial node representations are updated through graph propagation, and multihop reasoning is performed via traversing through the graph edges for each subsequent sub-task (e.g., paragra

Research Question

What is the impact of iterative retrieval agent depth (number of retrieval rounds) on final answer F1 score and retrieval precision across HotpotQA, 2WikiMultihopQA, and MuSiQue datasets when evaluated with controlled distractor insertion rates from 0-50%?

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