mlsanityData to 14 Oct 2026

Improving RAG for Personalization with Author Features and Contrastive Examples

arXiv 2504.08745 ↗First posted 24 Mar 2025cs.IR cs.AI cs.CLcited by 3

On the map: Retrieval-augmented generation → RAG pipeline design and evaluation

Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation. Builds on 21 of its 24 references that are in the map. First posted on arXiv 24 Mar 2025.

Builds on

21 of 24 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024The Impact of Quantization on Retrieval-Augmented Generation: An Analysis of Small LLMs10 Jun
  2. 2024Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation9 Apr
  3. 2024The Power of Noise: Redefining Retrieval for RAG Systems26 Jan

Other references · 18

  1. 2024The Llama 3 Herd of Modelscited by 14,715
  2. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,459
  3. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
  4. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,824
  5. 2024Gemma 2: Improving Open Language Models at a Practical Sizecited by 1,998
  6. 2021Unsupervised Dense Information Retrieval with Contrastive Learningcited by 1,216
  7. 2023LaMP: When Large Language Models Meet Personalizationcited by 437
  8. 2023A Survey on Large Language Models for Recommendationcited by 379
  9. 2023OpenChat: Advancing Open-source Language Models with Mixed-Quality Datacited by 252
  10. 2023Pearl: Personalizing Large Language Model Writing Assistants with Generation-Calibrated Retrieverscited by 92
  11. 2024Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuningcited by 90
  12. 2024Personalized Pieces: Efficient Personalized Large Language Models through Collaborative Effortscited by 74
6 more
  1. 2024HYDRA: Model Factorization Framework for Black-Box LLM Personalizationcited by 60
  2. 2023Contrastive Chain-of-Thought Promptingcited by 52
  3. 2023Teach LLMs to Personalize -- An Approach inspired by Writing Educationcited by 48
  4. 2024Comparing Retrieval-Augmentation and Parameter-Efficient Fine-Tuning for Privacy-Preserving Personalization of Large Language Modelscited by 21
  5. 2024Personalized Text Generation with Fine-Grained Linguistic Controlcited by 20
  6. 2024Understanding the Role of User Profile in the Personalization of Large Language Modelscited by 11

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2025Latent Inter-User Difference Modeling for LLM Personalizationcited by 21
  2. 2026IntPro: A Proxy Agent for Context-Aware Intent Understanding via Retrieval-conditioned Inferencecited by 0
  3. 2026ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding, but at What Cost?cited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Retrieval Augmented Generation with Collaborative Filtering for Personalized Text Generation
  2. 2025Iterative Critique-Refine Framework for Enhancing LLM Personalization
  3. 2025Personalized Text Generation with Contrastive Activation Steering
  4. 2025PrLM: Learning Explicit Reasoning for Personalized RAG via Contrastive Reward Optimization
  5. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey
  6. 2024Evaluation of Attribution Bias in Generator-Aware Retrieval-Augmented Large Language Models
  7. 2024A Survey on Retrieval-Augmented Text Generation for Large Language Models
  8. 2024PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization