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LaMP: When Large Language Models Meet Personalization

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arxiv 2304.11406 v4 pith:SYYPW6VF submitted 2023-04-22 cs.CL

Alireza Salemi , Sheshera Mysore , Michael Bendersky , Hamed Zamani This is my paper
classification cs.CL
keywords languagemodelslamptaskspersonalizationretrievalaugmentationbenchmark
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 10 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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