通过数据增强提升大模型多方面可控文本生成能力
A Lightweight Multi Aspect Controlled Text Generation Solution For Large Language Models
- 基于数据增强构建轻量级可控生成流程
- 准确率提升20%，各属性间相关性降低
- 适合需要精准控制生成内容的场景
大语言模型在指令微调后表现优异，但在目标任务缺乏高质量指令数据时效果不佳。多方面可控文本生成（MCTG）是典型挑战，现有数据集常存在偏差和属性相关性。本文提出轻量级MCTG流水线，通过数据增强引入更多控制属性与句子，缓解偏差与相关性问题。实验表明，经数据增强后，大模型在MCTG任务中准确率提升20%，属性间相关性显著下降，且增强数据可直接用于指令微调。
原文摘要 · Abstract (English)
Large language models (LLMs) show remarkable abilities with instruction tuning. However, they fail to achieve ideal tasks when lacking high-quality instruction tuning data on target tasks. Multi-Aspect Controllable Text Generation (MCTG) is a representative task for this dilemma, where aspect datasets are usually biased and correlated. Existing work exploits additional model structures and strategies for solutions, limiting adaptability to LLMs. To activate MCTG ability of LLMs, we propose a lightweight MCTG pipeline based on data augmentation. We analyze bias and correlations in traditional datasets, and address these concerns with augmented control attributes and sentences. Augmented datasets are feasible for instruction tuning. In our experiments, LLMs perform better in MCTG after data augmentation, with a 20% accuracy rise and less aspect correlations.
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