SetBERT: Enhancing Retrieval Performance for Boolean Logic and Set Operation Queries

• URL: http://arxiv.org/abs/2406.17282v2
• Date: Wed, 26 Jun 2024 14:38:31 GMT
• Title: SetBERT: Enhancing Retrieval Performance for Boolean Logic and Set Operation Queries
• Authors: Quan Mai, Susan Gauch, Douglas Adams,
• Abstract summary: We introduce SetBERT, a fine-tuned BERT-based model to enhance query embeddings for set operations and Boolean logic queries. Our experiments reveal that SetBERT-base not only significantly outperforms BERT-base but also achieves performance comparable to the much larger BERT-large model.
• Score: 0.8192907805418583
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce SetBERT, a fine-tuned BERT-based model designed to enhance query embeddings for set operations and Boolean logic queries, such as Intersection (AND), Difference (NOT), and Union (OR). SetBERT significantly improves retrieval performance for logic-structured queries, an area where both traditional and neural retrieval methods typically underperform. We propose an innovative use of inversed-contrastive loss, focusing on identifying the negative sentence, and fine-tuning BERT with a dataset generated via prompt GPT. Furthermore, we demonstrate that, unlike other BERT-based models, fine-tuning with triplet loss actually degrades performance for this specific task. Our experiments reveal that SetBERT-base not only significantly outperforms BERT-base (up to a 63% improvement in Recall) but also achieves performance comparable to the much larger BERT-large model, despite being only one-third the size.

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