LaMP: When Large Language Models Meet Personalization
Open accessAbstract
This paper highlights the importance of personalization in large language models and introduces the LaMP benchmark -a novel benchmark for training and evaluating language models for producing personalized outputs.LaMP offers a comprehensive evaluation framework with diverse language tasks and multiple entries for each user profile.It consists of seven personalized tasks, spanning three text classification and four text generation tasks.We additionally propose two retrieval augmentation approaches that retrieve personal items from each user profile for personalizing language model outputs.To this aim, we study various retrieval models, including term matching, semantic matching, and time-aware methods.Extensive experiments on LaMP for zero-shot and fine-tuned language models demonstrate the efficacy of the proposed retrieval augmentation approach and highlight the impact of personalization in various natural language tasks.
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Real-user memory benchmarks like LaMP and MSC are limited in length, offering only short sequences that do not stress the long-context windows available in modern LLMs.
“Others with real user records, like LaMP (Salemi et al., 2024) and MSC (Xu et al., 2022), are limited in length, offering only short sequences that do not stress the long-context windows available in modern LLMs.”
From MemoryCD: Benchmarking Long-Context User Memory of LLM Agents for Lifelong Cross-Domain Personalization (2026) · cites this paper as Salemi et al., 2024
The Introduction says LaMP user profiles are often large enough to exceed LLM length limits, and Section 3 describes each profile as a potentially large collection of data points. That is contrary to the attributed claim that LaMP offers only short sequences, although the paper does not assess whether its profiles stress the context windows of modern LLMs. (LLM, conf=0.85)
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Experimental — accuracy not yet measuredA smaller line of work evaluates memory using real user interaction histories, including MSC and LaMP.
“A smaller line of work evaluates memory using real user interaction histories, including MSC (Xu et al., 2022) and LaMP (Salemi et al., 2024).”
From MemoryCD: Benchmarking Long-Context User Memory of LLM Agents for Lifelong Cross-Domain Personalization (2026) · cites this paper as Salemi et al., 2024
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- Authors
- Salemi, A., Mysore, S., Bendersky, M., & Zamani, H.
- Affiliations
- University of Massachusetts Amherst (US)
- Google (United States) (US)
- Venue
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 7370-7392
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4402671806
- Cited by
- 109
- References
- 11
- Citations by year
- 2026: 42 · 2025: 47 · 2024: 11 · 2023: 9
- FWCI
- 29.51
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion, cc-by-nc-sa)
- arXiv (Cornell University)
- Funders
- National Science Foundation (2143434)
- Office of Naval Research (N000142212688)
- 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
- personalized language models, retrieval augmentation, user profiles, text classification, text generation, semantic matching, zero-shot learning, fine-tuning
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