mlsanityData to 14 Oct 2026

UserIdentifier: Implicit User Representations for Simple and Effective Personalized Sentiment Analysis

arXiv 2110.00135 ↗First posted 1 Oct 2021cs.LG cs.AI cs.CLcited by 28

Not on the map. Why?

Abstract (arXiv)

Global models are trained to be as generalizable as possible, with user invariance considered desirable since the models are shared across multitudes of users. As such, these models are often unable to produce personalized responses for individual users, based on their data. Contrary to widely-used personalization techniques based on few-shot learning, we propose UserIdentifier, a novel scheme for training a single shared model for all users. Our approach produces personalized responses by adding fixed, non-trainable user identifiers to the input data. We empirically demonstrate that this proposed method outperforms the prefix-tuning based state-of-the-art approach by up to 13%, on a suite of sentiment analysis datasets. We also show that, unlike prior work, this method needs neither any additional model parameters nor any extra rounds of few-shot fine-tuning.

Key facts. Cited by 28 papers in the map (data through 14 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 1 Oct 2021.

Builds on

15 of 30 references.

References · 15

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,915
  4. 2016Federated Learning: Strategies for Improving Communication Efficiencycited by 1,475
  5. 2018LEAF: A Benchmark for Federated Settingscited by 792
  6. 2019Fair Resource Allocation in Federated Learningcited by 394
  7. 2020Adaptive Personalized Federated Learningcited by 358
  8. 2020Three Approaches for Personalization with Applications to Federated Learningcited by 325
  9. 2021A Field Guide to Federated Optimizationcited by 315
  10. 2020Survey of Personalization Techniques for Federated Learningcited by 145
  11. 2018Training Millions of Personalized Dialogue Agentscited by 126
  12. 2020Privacy in Deep Learning: A Surveycited by 71
3 more
  1. 2020A Probabilistic Formulation of Unsupervised Text Style Transfercited by 70
  2. 2021Benchmarking Differential Privacy and Federated Learning for BERT Modelscited by 28
  3. 2015Overcoming Language Variation in Sentiment Analysis with Social Attentioncited by 23

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 6; 2024: 14; 2025: 4; 2026: 2.

28 papers.

Sort:

By area:

  1. 2023LaMP: When Large Language Models Meet Personalizationcited by 437
  2. 2024Optimization Methods for Personalizing Large Language Models through Retrieval Augmentationcited by 99
  3. 2024LongLaMP: A Benchmark for Personalized Long-form Text Generationcited by 97
  4. 2023Personalisation within bounds: A risk taxonomy and policy framework for the alignment of large language models with personalised feedbackcited by 87
  5. 2025A Survey of Personalized Large Language Models: Progress and Future Directionscited by 71
  6. 2024Large Language Models Empowered Personalized Web Agentscited by 66
  7. 2024Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinementcited by 49
  8. 2024Personalized Large Language Modelscited by 44
  9. 2024User Embedding Model for Personalized Language Promptingcited by 28
  10. 2025A Survey of Personalization: From RAG to Agentcited by 27
  11. 2024Towards Unified Multi-Modal Personalization: Large Vision-Language Models for Generative Recommendation and Beyondcited by 25
  12. 2022Unifying Data Perspectivism and Personalization: An Application to Social Normscited by 22
16 more
  1. 2024Personalized Text Generation with Fine-Grained Linguistic Controlcited by 20
  2. 2024ComPO: Community Preferences for Language Model Personalizationcited by 19
  3. 2024PERSOMA: PERsonalized SOft ProMpt Adapter Architecture for Personalized Language Promptingcited by 14
  4. 2023Large Human Language Models: A Need and the Challengescited by 11
  5. 2024Corpus Considerations for Annotator Modeling and Scalingcited by 8
  6. 2022Human Language Modelingcited by 5
  7. 2023Modeling Uncertainty in Personalized Emotion Prediction with Normalizing Flowscited by 5
  8. 2024Learning User Embeddings from Human Gaze for Personalised Saliency Predictioncited by 4
  9. 2024Multi-Objective Linguistic Control of Large Language Modelscited by 4
  10. 2024My Words Imply Your Opinion: Reader Agent-based Propagation Enhancement for Personalized Implicit Emotion Analysiscited by 4
  11. 2023Reference-less Analysis of Context Specificity in Translation with Personalised Language Modelscited by 3
  12. 2023PerPLM: Personalized Fine-tuning of Pretrained Language Models via Writer-specific Intermediate Learning and Promptscited by 3
  13. 2025Syntax-Guided Diffusion Language Models with User-Integrated Personalizationcited by 0
  14. 2025Reflective Personalization Optimization: A Post-hoc Rewriting Framework for Black-Box Large Language Modelscited by 0
  15. 2026Addressing the Ecological Fallacy in Larger LMs with Human Contextcited by 0
  16. 2026From Affect Prediction to Affect Forecasting: Evidence for Distinct Information Sources in Longitudinal Textcited by 0