AGRaME: Any-Granularity Ranking with Multi-Vector Embeddings
Revanth Gangi Reddy, Omar Attia, Yunyao Li, Heng Ji, Saloni Potdar
Abstract
Ranking is a fundamental problem in search, however, existing ranking algorithms usually restrict the granularity of ranking to full passages or require a specific dense index for each desired level of granularity. Such lack of flexibility in granularity negatively affects many applications that can benefit from more granular ranking, such as sentence-level ranking for open-domain QA, or proposition-level ranking for attribution. In this work, we introduce the idea of any-granularity ranking which leverages multi-vector embeddings to rank at varying levels of granularity while maintaining encoding at a single (coarser) level of granularity. We propose a multi-granular contrastive loss for training multi-vector approaches and validate its utility with both sentences and propositions as ranking units. Finally, we demonstrate the application of proposition-level ranking to post-hoc citation addition in retrieval-augmented generation, surpassing the performance of prompt-driven citation generation.- Anthology ID:
- 2024.emnlp-main.490
- Volume:
- Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8630–8641
- Language:
- URL:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.emnlp-main.490/
- DOI:
- 10.18653/v1/2024.emnlp-main.490
- Cite (ACL):
- Revanth Gangi Reddy, Omar Attia, Yunyao Li, Heng Ji, and Saloni Potdar. 2024. AGRaME: Any-Granularity Ranking with Multi-Vector Embeddings. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 8630–8641, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- AGRaME: Any-Granularity Ranking with Multi-Vector Embeddings (Gangi Reddy et al., EMNLP 2024)
- PDF:
- https://preview.aclanthology.org/build-pipeline-with-new-library/2024.emnlp-main.490.pdf