Papers by Ehud Rivlin
Breaking the Language Barrier: Can Direct Inference Outperform Pre-Translation in Multilingual LLM Applications? (2024.naacl-short)
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Yotam Intrator, Matan Halfon, Roman Goldenberg, Reut Tsarfaty, Matan Eyal, Ehud Rivlin, Yossi Matias, Natalia Aizenberg
| Challenge: | Existing studies have focused on pre-translation, but there is still need for it . authors say that it is not universally necessary to translate large language models . |
| Approach: | They re-evaluate the need for pre-translation in the context of PaLM2 models . authors found that PaLM2-L consistently outperforms pre-translated in 94 out of 108 languages . |
| Outcome: | The proposed model outperforms pre-translation in 94 out of 108 languages and 6 benchmarks . authors argue that pre-translated inputs can be used to improve performance . |
On the Semantic Latent Space of Diffusion-Based Text-To-Speech Models (2024.acl-short)
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| Challenge: | Denoising Diffusion Models (DDMs) are a powerful generative tool for text-to-speech (TTS) but their semantic capabilities are unknown and control of synthesized speech’s vocal properties remains a challenge. |
| Approach: | They explore the latent space of frozen TTS models composed of latent bottleneck activations of the DDM’s denoiser and propose methods for finding semantic directions within it. |
| Outcome: | The proposed methods enable off-the-shelf audio editing without any training, architectural changes or data requirements. |