用作者特征和对比文本提升RAG的个性化生成效果
Improving RAG for Personalization with Author Features and Contrastive Examples
- 引入作者情感极性、高频词等细粒度特征增强上下文
- 加入其他作者的对比样本，显著提升风格识别准确率
- 相对基线提升15%，适合个性化内容生成研究者
基于检索增强生成（RAG）的个性化常因无法捕捉作者细微特征而失效。为此，我们向大语言模型提供作者特定特征，如平均情感极性、常用词汇，并结合该作者历史文本。提出新方法‘对比样例’：从其他作者中检索文档，帮助模型识别作者风格的独特之处。实验表明，添加命名实体、依存模式及高频词的若干句描述可显著提升个性化生成效果；结合特征与对比样例进一步提升性能，相对基线实现15%的相对改进，超越现有基准。结果证明细粒度特征对个性化至关重要，同时为RAG引入对比样例开辟了新方向。代码已公开。
原文摘要 · Abstract (English)
Personalization with retrieval-augmented generation (RAG) often fails to capture fine-grained features of authors, making it hard to identify their unique traits. To enrich the RAG context, we propose providing Large Language Models (LLMs) with author-specific features, such as average sentiment polarity and frequently used words, in addition to past samples from the author's profile. We introduce a new feature called Contrastive Examples: documents from other authors are retrieved to help LLM identify what makes an author's style unique in comparison to others. Our experiments show that adding a couple of sentences about the named entities, dependency patterns, and words a person uses frequently significantly improves personalized text generation. Combining features with contrastive examples boosts the performance further, achieving a relative 15% improvement over baseline RAG while outperforming the benchmarks. Our results show the value of fine-grained features for better personalization, while opening a new research dimension for including contrastive examples as a complement with RAG. We release our code publicly.
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