Papers with multi-vector approaches
SimLM: Pre-training with Representation Bottleneck for Dense Passage Retrieval (2023.acl-long)
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Liang Wang, Nan Yang, Xiaolong Huang, Binxing Jiao, Linjun Yang, Daxin Jiang, Rangan Majumder, Furu Wei
| Challenge: | SimLM uses a simple bottleneck architecture that learns to compress the passage information into a dense vector through self-supervised pre-training. |
| Approach: | They propose a simple yet effective pre-training method for dense passage retrieval that learns to compress the passage information into a dense vector through self-supervised pre-tuning. |
| Outcome: | The proposed method outperforms multi-vector approaches on large-scale passage retrieval datasets and shows significant improvements over baselines. |
AGRaME: Any-Granularity Ranking with Multi-Vector Embeddings (2024.emnlp-main)
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| Challenge: | Existing ranking algorithms restrict granularity to full passages or require a specific dense index for each desired level of granules. |
| Approach: | They propose a multi-vector ranking approach that leverages multi-vctor embeddings to rank at varying levels of granularity while maintaining encoding at a single (coarser) level of grail. |
| Outcome: | The proposed method surpasses prompt-driven citation generation by incorporating proposition-level ranking to post-hoc citation addition. |