mlsanityData to 7 Oct 2026

Guided Profile Generation Improves Personalization with LLMs

arXiv 2409.13093 ↗First posted 19 Sep 2024cs.CL cs.AIcited by 19

Not on the map. Why?

Abstract (arXiv)

In modern commercial systems, including Recommendation, Ranking, and E-Commerce platforms, there is a trend towards improving customer experiences by incorporating Personalization context as input into Large Language Models (LLMs). However, LLMs often struggle to effectively parse and utilize sparse and complex personal context without additional processing or contextual enrichment, underscoring the need for more sophisticated context understanding mechanisms. In this work, we propose Guided Profile Generation (GPG), a general method designed to generate personal profiles in natural language. As is observed, intermediate guided profile generation enables LLMs to summarize, and extract the important, distinctive features from the personal context into concise, descriptive sentences, precisely tailoring their generation more closely to an individual's unique habits and preferences. Our experimental results show that GPG improves LLM's personalization ability across different tasks, for example, it increases 37% accuracy in predicting personal preference compared to directly feeding the LLMs with raw personal context.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 30 of its 43 references that are in the map. First posted on arXiv 19 Sep 2024.

Builds on

30 of 43 references.

References · 30

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  4. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  5. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  6. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  7. 2020Measuring Massive Multitask Language Understandingcited by 7,768
  8. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,462
  9. 2023Improved Baselines with Visual Instruction Tuningcited by 4,362
  10. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  11. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  12. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
18 more
  1. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,855
  2. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,970
  3. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 888
  4. 2018Personalizing Dialogue Agents: I have a dog, do you have pets too?cited by 820
  5. 2015Image-based Recommendations on Styles and Substitutescited by 702
  6. 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 517
  7. 2023V*: Guided Visual Search as a Core Mechanism in Multimodal LLMscited by 459
  8. 2023LaMP: When Large Language Models Meet Personalizationcited by 425
  9. 2023Large Language Models are Few-Shot Health Learnerscited by 115
  10. 2023PALR: Personalization Aware LLMs for Recommendationcited by 96
  11. 2023Guiding Large Language Models via Directional Stimulus Promptingcited by 94
  12. 2019Generating Personalized Recipes from Historical User Preferencescited by 52
  13. 2023Teach LLMs to Personalize -- An Approach inspired by Writing Educationcited by 48
  14. 2023Creativity Support in the Age of Large Language Models: An Empirical Study Involving Emerging Writerscited by 32
  15. 2023Heterogeneous Graph Contrastive Learning for Recommendationcited by 28
  16. 2023Uncovering the Potential of ChatGPT for Discourse Analysis in Dialogue: An Empirical Studycited by 24
  17. 2024Planning and Editing What You Retrieve for Enhanced Tool Learningcited by 21
  18. 2024SemEval-2024 Task 9: BRAINTEASER: A Novel Task Defying Common Sensecited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 9; 2026: 10.

19 papers.

Sort:

By area:

  1. 2025Memory in the Age of AI Agentscited by 285
  2. 2025A Survey of Personalization: From RAG to Agentcited by 26
  3. 2025Population-Aligned Persona Generation for LLM-based Social Simulationcited by 20
  4. 2025Evaluating Personalized Tool-Augmented LLMs from the Perspectives of Personalization and Proactivitycited by 17
  5. 2025LettinGo: Explore User Profile Generation for Recommendation Systemcited by 11
  6. 2025MR.Rec: Synergizing Memory and Reasoning for Personalized Recommendation Assistant with LLMscited by 9
  7. 2025OASIS: Online Sample Selection for Continual Visual Instruction Tuningcited by 8
  8. 2026P-GenRM: Personalized Generative Reward Model with Test-time User-based Scalingcited by 4
  9. 2025Modeling and Optimizing User Preferences in AI Copilots: A Comprehensive Survey and Taxonomycited by 3
  10. 2025Personalized Reward Modeling for Text-to-Image Generationcited by 3
  11. 2026CURP: Codebook-based Continuous User Representation for Personalized Generation with LLMscited by 3
  12. 2026Exploiting contextual information to improve stance detection in informal political discourse with LLMscited by 2
7 more
  1. 2026DeltaMem: Towards Agentic Memory Management via Reinforcement Learningcited by 2
  2. 2026Offline Reasoning for Efficient Recommendation: LLM-Empowered Persona-Profiled Item Indexingcited by 1
  3. 2026Personagram: Bridging Personas and Product Design for Creative Ideation with Multimodal LLMscited by 0
  4. 2026ChildEval: When large language models meet children's personalitiescited by 0
  5. 2026In-Context Reward Adaptation for Robust Preference Modelingcited by 0
  6. 2026Beyond Isolated Behaviors: Hierarchical User Modeling for LLM Personalizationcited by 0
  7. 2026Learning to Adapt Cross-Domain Preferences via Meta-LoRA for LLM Personalizationcited by 0