Query Augmentation by Decoding Semantics from Brain Signals

• URL: http://arxiv.org/abs/2402.15708v2
• Date: Sun, 3 Mar 2024 09:18:07 GMT
• Title: Query Augmentation by Decoding Semantics from Brain Signals
• Authors: Ziyi Ye, Jingtao Zhan, Qingyao Ai, Yiqun Liu, Maarten de Rijke, Christina Lioma, Tuukka Ruotsalo
• Abstract summary: We propose Brain-Aug, which enhances a query by incorporating semantic information decoded from brain signals. Experimental results on fMRI datasets show that Brain-Aug produces semantically more accurate queries.
• Score: 61.89860975682576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Query augmentation is a crucial technique for refining semantically imprecise queries. Traditionally, query augmentation relies on extracting information from initially retrieved, potentially relevant documents. If the quality of the initially retrieved documents is low, then the effectiveness of query augmentation would be limited as well. We propose Brain-Aug, which enhances a query by incorporating semantic information decoded from brain signals. BrainAug generates the continuation of the original query with a prompt constructed with brain signal information and a ranking-oriented inference approach. Experimental results on fMRI (functional magnetic resonance imaging) datasets show that Brain-Aug produces semantically more accurate queries, leading to improved document ranking performance. Such improvement brought by brain signals is particularly notable for ambiguous queries.

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