Progress Ratio Embeddings: An Impatience Signal for Robust Length Control in Neural Text Generation
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917458184503296 |
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| author | Botcazou, Ivanhoé Amghar, Tassadit Lamprier, Sylvain Saubion, Frédéric |
| author_facet | Botcazou, Ivanhoé Amghar, Tassadit Lamprier, Sylvain Saubion, Frédéric |
| contents | Modern neural language models achieve high accuracy in text generation, yet precise control over generation length remains underdeveloped. In this paper, we first investigate a recent length control method based on Reverse Positional Embeddings (RPE) and show its limits when control is requested beyond the training distribution. In particular, using a discrete countdown signal tied to the absolute remaining token count leads to instability. To provide robust length control, we introduce Progress Ratio Embeddings (PRE), as continuous embeddings tied to a trigonometric impatience signal. PRE integrates seamlessly into standard Transformer architectures, providing stable length fidelity without degrading text accuracy under standard evaluation metrics. We further show that PRE generalizes well to unseen target lengths. Experiments on two widely used news-summarization benchmarks validate these findings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_06938 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Progress Ratio Embeddings: An Impatience Signal for Robust Length Control in Neural Text Generation Botcazou, Ivanhoé Amghar, Tassadit Lamprier, Sylvain Saubion, Frédéric Computation and Language Modern neural language models achieve high accuracy in text generation, yet precise control over generation length remains underdeveloped. In this paper, we first investigate a recent length control method based on Reverse Positional Embeddings (RPE) and show its limits when control is requested beyond the training distribution. In particular, using a discrete countdown signal tied to the absolute remaining token count leads to instability. To provide robust length control, we introduce Progress Ratio Embeddings (PRE), as continuous embeddings tied to a trigonometric impatience signal. PRE integrates seamlessly into standard Transformer architectures, providing stable length fidelity without degrading text accuracy under standard evaluation metrics. We further show that PRE generalizes well to unseen target lengths. Experiments on two widely used news-summarization benchmarks validate these findings. |
| title | Progress Ratio Embeddings: An Impatience Signal for Robust Length Control in Neural Text Generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2512.06938 |