Amazon Unveils 980 Million Parameter LLM Featuring Breakthrough Emergent Abilities
Amazon Develops LLM with “Emergent Abilities”
Researchers at Amazon have successfully trained a large language model (LLM) for text-to-speech, which they assert demonstrates “emergent” competencies. Named BASE TTS, this model boasts 980 million parameters, making it the most expansive text-to-speech model to date.
To explore the potential enhancements that models can achieve as they increase in size, the team trained various models on up to 100,000 hours of public domain speech data. Their medium-sized 400 million parameter version, trained on 10,000 hours of audio, showcased notable improvements in versatility and robustness when handling complex test sentences.
These test sentences included challenging features such as compound nouns, emotional tones, foreign terms, and punctuation—elements that frequently disrupt standard text-to-speech systems. Although BASE TTS did not flawlessly process these features, it significantly reduced errors in aspects like stress, intonation, and pronunciation compared to existing models.
The researchers pointed out, “These sentences are intentionally designed to present challenging tasks—none of which BASE TTS has been explicitly trained for.” Interestingly, they noted that the largest model, despite its enhanced parameters, did not display further capabilities beyond those found in the 400 million parameter version. This indicates that while the experimental process is promising, there are still optimal model sizes yet to be determined for achieving emergent abilities.
Additionally, BASE TTS is engineered to be lightweight and streamable, efficiently separating emotional and prosodic data. This feature may enable the transmission of natural-sounding spoken audio even over low-bandwidth connections.
The complete research paper on BASE TTS can be accessed on arXiv.
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