TL;DR
This work studies the repetition problem for encoder-decoder models, using both recurrent neural network (RNN) and trans-former architectures and proposes token loss dynamic reweighting (TLDR) that applies differentiable weights to individual token losses.
This work studies the repetition problem for encoder-decoder models, using both recurrent neural network (RNN) and trans-former architectures and proposes token loss dynamic reweighting (TLDR) that applies differentiable weights to individual token losses.
A large language model (LLM) is an AI model (typically a neural network) trained on a vast amount of text for natural language processing tasks, especially language generation. LLMs can typically generate, summarize, translate, and analyze text in many contexts. They are the basis for many modern chatbots, such as Chat...
After the launch of ChatGPT in autumn 2022, a lot of research has focussed on the quality and near-naturalness Large Language Model-based tools present in the texts they produce. While one area of research has focussed on the similarities and differences between machine-produced and human-produced output (e.g....
Large pretrained language models achieve strong performance on natural language processing tasks but are costly to deploy. Existing distillation methods in this domain almost exclusively assume architectural homogeneity between teacher and student, leaving cross-architecture transfer, well studied in computer vision, l...
Experimental results show that VALL-E outperforms the state-of-the-art zero-shot TTS system in terms of speech naturalness and speaker similarity and could preserve the speaker’s emotion and acoustic environment from the prompt in synthesis.
AWED, a word-level emission-delay metric defined relative to the acoustic end of each word, is introduced and post-train a DSM recognizer with GRPO under a reward that scores transcription accuracy and measured delay jointly, advancing the accuracy--latency Pareto frontier of streaming ASR without architectural change.
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