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Cited by 52Topic ModelingInformation Retrieval and Search BehaviorRecommender Systems and Techniques

Optimization Methods for Personalizing Large Language Models through Retrieval Augmentation

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Salemi, Alireza, Kallumadi, Surya, Zamani, Hamed·2024·Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
DOI 10.1145/3626772.3657783
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Abstract

This paper studies retrieval-augmented approaches for personalizing large language models (LLMs), which potentially have a substantial impact on various applications and domains. We propose the first attempt to optimize the retrieval models that deliver a limited number of personal documents to large language models for the purpose of personalized generation. We develop two optimization algorithms that solicit feedback from the downstream personalized generation tasks for retrieval optimization--one based on reinforcement learning whose reward function is defined using any arbitrary metric for personalized generation and another based on knowledge distillation from the downstream LLM to the retrieval model. This paper also introduces a pre- and post-generation retriever selection model that decides what retriever to choose for each LLM input. Extensive experiments on diverse tasks from the language model personalization (LaMP) benchmark reveal statistically significant improvements in six out of seven datasets.

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  • Memory-augmented methods improve the memory capabilities of LLMs without requiring fine-tuning.

    “Numerous approaches have demonstrated that memory-augmented methods improve the memory capabilities of LLMs without requiring fine-tuning, which is often impossible for API-based models [25, 42, 46].”

    From CAIM: Development and Evaluation of a Cognitive AI Memory Framework for Long-Term Interaction with Intelligent Agents (2026) · cites this paper as [46]

    The Introduction (p. 1) says retrieval of personal information can personalize an off-the-shelf LLM, and the retrieval-optimization discussion (p. 2) says the language model remains frozen. The Abstract (p. 1) reports improved personalization results. The paper does not establish improved LLM memory capabilities generally or discuss the claim that fine-tuning is often impossible for API-based models. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

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Cited by

Sorted by citations

Authors
Salemi, A., Kallumadi, S., & Zamani, H.
Affiliations
  • University of Massachusetts Amherst (US)
  • Lowe's (United States) (US)
ORCID
Venue
Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval
Publisher
ACM
Published
2024-07-10
Pages
752-762
DOI
10.1145/3626772.3657783
OpenAlex
W4400525546
Cited by
52
References
3
Citations by year
2026: 17 · 2025: 26 · 2024: 9
FWCI
15.37
Type
conference paper
Language
en
Open access
Licence
Locations
  • International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion, cc-by)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
Keywords
retrieval-augmented generation, knowledge distillation, feedback-based optimization

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