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arXiv · 2406.17282

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

Quan Mai·Susan Gauch·Douglas Adams
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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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Quan Mai, Susan Gauch, Douglas Adams. 2024-06-25. SetBERT: Enhancing Retrieval Performance for Boolean Logic and Set Operation Queries. https://arxiv.org/abs/2406.17282

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