Query Augmentation by Decoding Semantics from Brain Signals
This addresses query augmentation for information retrieval systems, but it is incremental as it builds on existing methods by adding brain signal data.
The paper tackles the problem of refining semantically imprecise queries by proposing Brain-Aug, which enhances queries using semantic information decoded from brain signals, resulting in improved document ranking performance, particularly for ambiguous queries.
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.