Improving RAG for Personalization with Author Features and Contrastive Examples

• URL: http://arxiv.org/abs/2504.08745v1
• Date: Mon, 24 Mar 2025 01:41:22 GMT
• Title: Improving RAG for Personalization with Author Features and Contrastive Examples
• Authors: Mert Yazan, Suzan Verberne, Frederik Situmeang,
• Abstract summary: Personalization with retrieval-augmented generation (RAG) often fails to capture fine-grained features of authors.<n>We introduce Contrastive Examples: documents from other authors are retrieved to help LLM identify what makes an author's style unique in comparison to others.<n>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.
• Score: 2.6968321526169503
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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