Papers by Xixin Hu
Autoregressive Speech Synthesis without Vector Quantization (2025.acl-long)
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Lingwei Meng, Long Zhou, Shujie Liu, Sanyuan Chen, Bing Han, Shujie Hu, Yanqing Liu, Jinyu Li, Sheng Zhao, Xixin Wu, Helen M. Meng, Furu Wei
| Challenge: | MELLE is a novel language modeling approach for text-to-speech synthesis that generates continuous tokens from text . authors demonstrate that it reduces the need for vector quantization and improves model robustness . |
| Approach: | They propose to autoregressively generate continuous mel-spectrogram frames directly from text condition, bypassing vector quantization. |
| Outcome: | The proposed model achieves superior performance across multiple metrics and is more streamlined. |
Rethinking Machine Ethics – Can LLMs Perform Moral Reasoning through the Lens of Moral Theories? (2024.findings-naacl)
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| Challenge: | Existing approaches to making moral judgments are mostly bottom-up and lack explainability. |
| Approach: | They propose a top-down framework to steer Large Language Models to perform moral reasoning with well-established moral theories. |
| Outcome: | The proposed framework can integrate various moral theories on moral datasets. |
Logical Form Generation via Multi-task Learning for Complex Question Answering over Knowledge Bases (2022.coling-1)
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| Challenge: | Existing generation-based KBQA methods that translate natural language questions to executable logical forms are proving promising but noise introduced can lead to incorrect results. |
| Approach: | They propose a Generation-based KBQA method that uses auxiliary information to enhance logical form generation by combining unseen KB items with novel combinations. |
| Outcome: | The proposed method achieves state-of-the-art results on ComplexWebQuestions and WebQuestIONSSP datasets. |