Papers with MoS
Continuous Speech Tokenizer in Text To Speech (2025.findings-naacl)
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| Challenge: | Autoregressive modeling is a common method for processing language sequences and is effective in token prediction. |
| Approach: | They propose a text-to-speech model based on continuous speech tokens and a continuous tokenizer for speech compression. |
| Outcome: | The proposed model has better continuity and higher estimated Mean Opinion Scores (MoS) this is attributed to better information preservation rate across low and high frequencies in the frequency domain. |
Softmax Bottleneck Makes Language Models Unable to Represent Multi-mode Word Distributions (2022.acl-long)
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| Challenge: | Existing language models can't predict the probability distribution over next words or masked words because of a single hidden state. |
| Approach: | They propose a multi-facet softmax to address the limitations of softmax . they propose LMs that can predict the next word distribution better as their sizes grow . |
| Outcome: | The proposed model achieves two-fold improvements in the perplexity of GPT-2 and BERT. |
Mixture-of-Skills: Learning to Optimize Data Usage for Fine-Tuning Large Language Models (2024.emnlp-main)
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| Challenge: | Large language models are fine-tuned on diverse datasets to develop a range of skills . each skill has unique characteristics, and datasets are heterogeneous and imbalanced . a general, model-agnostic, reinforcement learning framework is proposed to optimize data usage . |
| Approach: | They propose a general, model-agnostic, reinforcement learning framework that optimizes data usage automatically during the fine-tuning process. |
| Outcome: | The proposed framework optimizes data usage automatically during the fine-tuning process. |