mlsanityData to 14 Oct 2026

Large Language Models, and LLM-Based Agents, Should Be Used to Enhance the Digital Public Sphere

arXiv 2410.12123 ↗First posted 15 Oct 2024cs.CY cs.IRcited by 1

On the map: LLM agents and tool use → Agentic recommender systems

Abstract (arXiv)

This paper argues that large language model-based recommenders can displace today's attention-allocation machinery. LLM-based recommenders would ingest open-web content, infer a user's natural-language goals, and present information that matches their reflective preferences. Properly designed, they could deliver personalization without industrial-scale data hoarding, return control to individuals, optimize for genuine ends rather than click-through proxies, and support autonomous attention management. Synthesizing evidence of current systems' harms with recent work on LLM-driven pipelines, we identify four key research hurdles: generating candidates without centralized data, maintaining computational efficiency, modeling preferences robustly, and defending against prompt-injection. None looks prohibitive; surmounting them would steer the digital public sphere toward democratic, human-centered values.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Agentic recommender systems, one of 16 directions in LLM agents and tool use. Builds on 35 of its 97 references that are in the map. First posted on arXiv 15 Oct 2024.

Builds on

35 of 97 references.

References · 35

  1. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  2. 2023MetaGPT: Meta Programming for A Multi-Agent Collaborative Frameworkcited by 1,808
  3. 2023Language Models Don't Always Say What They Think: Unfaithful Explanations in Chain-of-Thought Promptingcited by 1,122
  4. 2024Cambrian-1: A Fully Open, Vision-Centric Exploration of Multimodal LLMscited by 838
  5. 2024GPT-4V(ision) is a Generalist Web Agent, if Groundedcited by 563
  6. 2024InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agentscited by 538
  7. 2023On the Planning Abilities of Large Language Models : A Critical Investigationcited by 312
  8. 2023Chat-REC: Towards Interactive and Explainable LLMs-Augmented Recommender Systemcited by 242
  9. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 189
  10. 2024A Roadmap to Pluralistic Alignmentcited by 184
  11. 2023How Can Recommender Systems Benefit from Large Language Models: A Surveycited by 142
  12. 2023Eliciting Human Preferences with Language Modelscited by 92
23 more
  1. 2023Training Socially Aligned Language Models on Simulated Social Interactionscited by 78
  2. 2023Leveraging Large Language Models in Conversational Recommender Systemscited by 63
  3. 2023Dynamic Planning with a LLMcited by 61
  4. 2024A Review of Modern Recommender Systems Using Generative Models (Gen-RecSys)cited by 58
  5. 2023CLOVA: A Closed-Loop Visual Assistant with Tool Usage and Updatecited by 43
  6. 2024Using Counterfactual Tasks to Evaluate the Generality of Analogical Reasoning in Large Language Modelscited by 38
  7. 2024Algorithmic progress in language modelscited by 31
  8. 2023Engagement, User Satisfaction, and the Amplification of Divisive Content on Social Mediacited by 30
  9. 2023Embedding Democratic Values into Social Media AIs via Societal Objective Functionscited by 25
  10. 2022On Natural Language User Profiles for Transparent and Scrutable Recommendationcited by 23
  11. 2020From Optimizing Engagement to Measuring Valuecited by 21
  12. 2024Enhancing Recommendation Diversity by Re-ranking with Large Language Modelscited by 19
  13. 2024Language-Based User Profiles for Recommendationcited by 18
  14. 2023Bridging Systems: Open Problems for Countering Destructive Divisiveness across Ranking, Recommenders, and Governancecited by 17
  15. 2023ChatGPT's One-year Anniversary: Are Open-Source Large Language Models Catching up?cited by 15
  16. 2024Recommendation with Generative Modelscited by 13
  17. 2024Exploring the Impact of Large Language Models on Recommender Systems: An Extensive Reviewcited by 12
  18. 2024What We Know About Using Non-Engagement Signals in Content Rankingcited by 6
  19. 2024Frontier AI Ethics: Anticipating and Evaluating the Societal Impacts of Language Model Agentscited by 5
  20. 2022Mostra: A Flexible Balancing Framework to Trade-off User, Artist and Platform Objectives for Music Sequencingcited by 4
  21. 2022Legitimacy, Authority, and Democratic Duties of Explanationcited by 4
  22. 2022Language Models Understand Us, Poorlycited by 3
  23. 2024Form-From: A Design Space of Social Media Systemscited by 2

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

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  1. 2026From Hidden Profiles to Governable Personalization: Recommender Systems in the Age of LLM Agents
  2. 2024Towards Next-Generation LLM-based Recommender Systems: A Survey and Beyond
  3. 2025A Survey of Large Language Model Empowered Agents for Recommendation and Search: Towards Next-Generation Information Retrieval
  4. 2024All Roads Lead to Rome: Unveiling the Trajectory of Recommender Systems Across the LLM Era
  5. 2025Generative Large Recommendation Models: Emerging Trends in LLMs for Recommendation
  6. 2023ONCE: Boosting Content-based Recommendation with Both Open- and Closed-source Large Language Models
  7. 2025A Comprehensive Review on Harnessing Large Language Models to Overcome Recommender System Challenges
  8. 2026LLM-Based User Personas for Recommendations at Scale