Papers with auto-regressive baselines
DiffS2UT: A Semantic Preserving Diffusion Model for Textless Direct Speech-to-Speech Translation (2023.emnlp-main)
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| Challenge: | Existing models for speech generation are not efficient due to low information density of speech data. |
| Approach: | They propose a method to integrate discrete diffusion models into speech generation tasks . they propose to apply diffusion forward process while employing diffusion backward process . |
| Outcome: | The proposed model achieves comparable results to the auto-regressive baselines with significantly fewer decoding steps (50 steps). |
EQUIP: EQUivariant preserving In-Place updates for Efficient Token Pruning (2026.acl-long)
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| Challenge: | Token-pruning methods cause "holes" in KV tensors, posing major challenges . equip reduces recomputation of rotation operations through in-place update, caching and re-indexing . |
| Approach: | They propose an EQUIP-based in-place token update mechanism that preserves the equivariance property of the operations performed in the attention computation. |
| Outcome: | EQUIP reduces recomputation of rotation operations and reduces eviction overheads . it achieves geomean speedups of 1.62 (or 1.47) over StreamingLLM and 3.45 ( or 1.86) |