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arXiv Computation and Language By Xi Yang, Guodong Liu, Chuqin Li, Fan Wu, Ergin Soysal, Min Jiang, Xing He, Jiang Bian, Yi Guo, Shams Zaman, Thomas Fuchs, Yonghui Wu

Exploring Post-Training Alignment of Small Language Models for Biomedical Data-to-Text Generation: A Case Study of Medication Leaflet

Read the original on arXiv Computation and Language →

The study investigates how post‑training alignment techniques affect small language models (SLMs) for generating medication leaflets from structured biomedical data. Four alignment methods—supervised fine‑tuning (SFT), direct preference optimization (DPO), odds ratio preference optimization (ORPO), and group relative policy optimization (GRPO)—are evaluated using Qwen‑2.5 models, with a new FDALeaflets dataset derived from OpenFDA drug labels for zero‑shot cross‑regulatory testing. Results show that aligned SLMs can outperform GPT‑5 on reference‑based metrics, while ORPO and GRPO achieve the highest semantic textual similarity scores in‑domain and on FDALeaflets, respectively, highlighting the importance of alignment for semantic fidelity and generalization.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

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