Efficient Financial Language Understanding via Distillation with Synthetic Data
Large instruction-following models are powerful but costly to deploy, particularly in finance, where labelled data are limited by confidentiality and expert annotation cost. We present an efficient framework for financial sentiment analysis through distillation with synthetic data, transferring knowledge from a large instruction-tuned teacher to compact student models. The framework is designed for low-resource conditions, where a small set of real examples are collected and labelled by hand. The framework then clusters the examples and uses the clusters to select seeds for generating synthetic examples via structured few-shot prompting. Experiments show that clustering-based seed selecti...
| Authors | Wen-Fong, Huang, Edwin Simpson |
|---|---|
| Primary category | cs.CL |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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