Rhyme-aware Chinese lyric generator based on GPT
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Abstract (arXiv)
Neural language representation models such as GPT, pre-trained on large-scale corpora, can effectively capture rich semantic patterns from plain text and be fine-tuned to consistently improve natural language generation performance. However, existing pre-trained language models used to generate lyrics rarely consider rhyme information, which is crucial in lyrics. Using a pre-trained model directly results in poor performance. To enhance the rhyming quality of generated lyrics, we incorporate integrated rhyme information into our model, thereby improving lyric generation performance.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). Builds on 7 of its 8 references that are in the map. First posted on arXiv 19 Aug 2024.
Builds on
7 of 8 references.
References · 7
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2023GPT-4 Technical Reportcited by 18,396
- 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
- 2019ERNIE: Enhanced Language Representation with Informative Entitiescited by 528
- 2017Flexible and Creative Chinese Poetry Generation Using Neural Memorycited by 9
- 2024LIVE: Learnable In-Context Vector for Visual Question Answeringcited by 5
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Citing papers per year: 2024: 6; 2025: 3; 2026: 1.
10 papers.
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- 2025Coarse-to-Fine Personalized LLM Impressions for Streamlined Radiology Reports19 Aug 2025
- 2025A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planning8 Jun 2025
- 2025Gradient Boosting Decision Tree with LSTM for Investment Prediction29 May 2025
- 2024ShadowKV: KV Cache in Shadows for High-Throughput Long-Context LLM Inference28 Oct 2024
- 2024Enhancing Exchange Rate Forecasting with Explainable Deep Learning Models25 Oct 2024
- 2024Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments23 Sep 2024
- 2024Comprehensive Overview of Artificial Intelligence Applications in Modern Industries19 Sep 2024
- 2024Harnessing LLMs for API Interactions: A Framework for Classification and Synthetic Data Generation18 Sep 2024
- 2024PriorDrive: Enhancing Online HD Mapping with Unified Vector Priors9 Sep 2024