Papers with multi-vector representations
ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction (2022.naacl-main)
Copied to clipboard
| Challenge: | Neural information retrieval (IR) methods encode queries and documents into single vectors, but late interaction models produce multi-vector representations at the granularity of each token. |
| Approach: | They propose a retrieval method that couples an aggressive residual compression mechanism with a denoised supervision strategy to improve the quality and space footprint of late interaction. |
| Outcome: | The proposed retriever improves quality and space footprint of late interaction models while reducing space footprint by 6–10x. |
Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity (2022.naacl-main)
Copied to clipboard
| Challenge: | Using co-citations, we can train a model that matches aspects of papers to document level similarity. |
| Approach: | They propose a model that matches fine-grained aspects of papers and aggregates them into a document level similarity model using a naturally-occurring source of supervision: co-citations. |
| Outcome: | The proposed model improves performance on document similarity tasks in four datasets and achieves competitive results. |