Papers with dense retrieval training
GRITHopper: Decomposition-Free Multi-Hop Dense Retrieval (2026.eacl-long)
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| Challenge: | Decomposition-based multi-hop retrieval methods rely on autoregressive steps to break down complex queries, which breaks end-to-end differentiability and is computationally expensive. |
| Approach: | They propose a multi-hop dense retrieval model that integrates causal language modeling with dense retrievals. |
| Outcome: | The proposed model outperforms existing methods on in-distribution and out-of-difference benchmarks. |
Reduce Catastrophic Forgetting of Dense Retrieval Training with Teleportation Negatives (2022.emnlp-main)
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| Challenge: | Recent research shows strong empirical advantages of dense retrieval in various information access scenarios, such as openQA. |
| Approach: | They propose a method which accumulates momentum negatives from past iterations and approximates future iteration with lookahead negatives as "teleportations" on web search and OpenQA, ANCE-Tele outperforms previous state-of-the-art systems of similar size and eliminates the dependency on sparse retrieval negatives. |
| Outcome: | The proposed method outperforms previous state-of-the-art systems on web search and OpenQA and is competitive among systems with significantly more parameters. |