Papers by Martin Flechl
Linear-Time and Constant-Memory Text Embeddings Based on Recurrent Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Existing work on recurrent models for text embedding is limited to small task-specific models. |
| Approach: | They propose a vertically chunked inference strategy that enables fast embedding generation with memory usage that becomes constant in the input length once it exceeds the vertical chunk size. |
| Outcome: | The proposed architectures achieve competitive performance across benchmarks while maintaining a substantially smaller memory footprint compared to transformer-based models. |