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

• URL: http://arxiv.org/abs/2410.12123v3
• Date: Wed, 02 Jul 2025 01:22:47 GMT
• Title: Large Language Models, and LLM-Based Agents, Should Be Used to Enhance the Digital Public Sphere
• Authors: Seth Lazar, Luke Thorburn, Tian Jin, Luca Belli,
• Abstract summary: We argue that large language model-based recommenders can displace today's attention-allocation machinery.<n>They would ingest open-web content, infer a user's natural-injection goals, and present information that matches their reflective preferences.
• Score: 6.171497648710294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Aligning Large Vision-Language Models by Deep Reinforcement Learning and Direct Preference Optimization [3.6275547549769507]
  Large Vision-Language Models (LVLMs) or multimodal large language models represent a significant advancement in artificial intelligence.<n>Fine-tuning these models for aligning with human values or engaging in specific tasks or behaviors remains a critical challenge.<n>This overview explores paradigms for fine-tuning LVLMs, highlighting how DRL and DPO techniques can be used to align models with human preferences and values.
  arXiv  Detail & Related papers  (2025-09-08T14:47:57Z)
• DPO Learning with LLMs-Judge Signal for Computer Use Agents [9.454381108993832]
  Computer use agents (CUA) are systems that automatically interact with graphical user interfaces (GUIs) to complete tasks.<n>We develop a lightweight vision-language model that runs entirely on local machines.
  arXiv  Detail & Related papers  (2025-06-03T17:27:04Z)
• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• FedDTPT: Federated Discrete and Transferable Prompt Tuning for Black-Box Large Language Models [14.719919025265224]
  Fine-tuning large language models (LLMs) with data from specific scenarios poses privacy leakage risks. We propose for the first time a federated discrete and transferable prompt tuning, namely FedDTPT, for black-box large language models. Our approach achieves higher accuracy, reduced communication overhead, and robustness to non-iid data in a black-box setting.
  arXiv  Detail & Related papers  (2024-11-01T19:19:23Z)
• Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization [62.16747639440893]
  Large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering.<n>Our proposed framework incorporates retrieval-augmented generation (RAG) to enhance the system's ability to acquire domain-specific knowledge and generate solutions.
  arXiv  Detail & Related papers  (2024-08-07T08:43:32Z)
• Lifelong Personalized Low-Rank Adaptation of Large Language Models for Recommendation [50.837277466987345]
  We focus on the field of large language models (LLMs) for recommendation. We propose RecLoRA, which incorporates a Personalized LoRA module that maintains independent LoRAs for different users. We also design a Few2Many Learning Strategy, using a conventional recommendation model as a lens to magnify small training spaces to full spaces.
  arXiv  Detail & Related papers  (2024-08-07T04:20:28Z)
• Robust Utility-Preserving Text Anonymization Based on Large Language Models [80.5266278002083]
  Anonymizing text that contains sensitive information is crucial for a wide range of applications.<n>Existing techniques face the emerging challenges of the re-identification ability of large language models.<n>We propose a framework composed of three key components: a privacy evaluator, a utility evaluator, and an optimization component.
  arXiv  Detail & Related papers  (2024-07-16T14:28:56Z)
• Large Language Models for Power Scheduling: A User-Centric Approach [6.335540414370735]
  We introduce a novel architecture for resource scheduling problems by converting an arbitrary user's voice request (VRQ) into a resource allocation vector. Specifically, we design an LLM intent recognition agent to translate the request into an optimization problem (OP), an LLM OP parameter identification agent, and an OP solving agent.
  arXiv  Detail & Related papers  (2024-06-29T15:47:28Z)
• Opt-Out: Investigating Entity-Level Unlearning for Large Language Models via Optimal Transport [37.172662930947446]
  Large language models inadvertently disclose private, sensitive information to their users.<n>We introduce Opt-Out, an optimal transport-based unlearning method.<n>We also present the first entity-level unlearning dataset.
  arXiv  Detail & Related papers  (2024-06-18T06:54:05Z)
• The Frontier of Data Erasure: Machine Unlearning for Large Language Models [56.26002631481726]
  Large Language Models (LLMs) are foundational to AI advancements. LLMs pose risks by potentially memorizing and disseminating sensitive, biased, or copyrighted information. Machine unlearning emerges as a cutting-edge solution to mitigate these concerns.
  arXiv  Detail & Related papers  (2024-03-23T09:26:15Z)
• Recourse for reclamation: Chatting with generative language models [2.877217169371665]
  We extend the concept of algorithmic recourse to generative language models. We provide users a novel mechanism to achieve their desired prediction by dynamically setting thresholds for toxicity filtering. A pilot study supports the potential of our proposed recourse mechanism.
  arXiv  Detail & Related papers  (2024-03-21T15:14:25Z)
• Bayesian Preference Elicitation with Language Models [82.58230273253939]
  We introduce OPEN, a framework that uses BOED to guide the choice of informative questions and an LM to extract features. In user studies, we find that OPEN outperforms existing LM- and BOED-based methods for preference elicitation.
  arXiv  Detail & Related papers  (2024-03-08T18:57:52Z)
• Emerging Synergies Between Large Language Models and Machine Learning in Ecommerce Recommendations [19.405233437533713]
  Large language models (LLMs) have superior capabilities in basic tasks of language understanding and generation. We introduce a representative approach to learning user and item representations using LLM as a feature encoder. We then reviewed the latest advances in LLMs techniques for collaborative filtering enhanced recommendation systems.
  arXiv  Detail & Related papers  (2024-03-05T08:31:00Z)
• Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models [52.98743860365194]
  We propose a new fine-tuning method called Self-Play fIne-tuNing (SPIN) At the heart of SPIN lies a self-play mechanism, where the LLM refines its capability by playing against instances of itself. This sheds light on the promise of self-play, enabling the achievement of human-level performance in LLMs without the need for expert opponents.
  arXiv  Detail & Related papers  (2024-01-02T18:53:13Z)
• Adapting LLMs for Efficient, Personalized Information Retrieval: Methods and Implications [0.7832189413179361]
  Large Language Models (LLMs) excel in comprehending and generating human-like text. This paper explores strategies for integrating Language Models (LLMs) with Information Retrieval (IR) systems.
  arXiv  Detail & Related papers  (2023-11-21T02:01:01Z)
• On Generative Agents in Recommendation [58.42840923200071]
  Agent4Rec is a user simulator in recommendation based on Large Language Models. Each agent interacts with personalized recommender models in a page-by-page manner.
  arXiv  Detail & Related papers  (2023-10-16T06:41:16Z)
• Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models [31.509994889286183]
  We introduce Language Agent Tree Search (LATS) -- the first general framework that synergizes the capabilities of language models (LMs) in reasoning, acting, and planning. A key feature of our approach is the incorporation of an environment for external feedback, which offers a more deliberate and adaptive problem-solving mechanism. LATS achieves state-of-the-art pass@1 accuracy (92.7%) for programming on HumanEval with GPT-4 and demonstrates gradient-free performance (average score of 75.9) comparable to gradient-based fine-tuning for web navigation on WebShop with GPT
  arXiv  Detail & Related papers  (2023-10-06T17:55:11Z)
• Retroformer: Retrospective Large Language Agents with Policy Gradient Optimization [103.70896967077294]
  This paper introduces a principled framework for reinforcing large language agents by learning a retrospective model. Our proposed agent architecture learns from rewards across multiple environments and tasks, for fine-tuning a pre-trained language model. Experimental results on various tasks demonstrate that the language agents improve over time.
  arXiv  Detail & Related papers  (2023-08-04T06:14:23Z)
• Exploring Large Language Model for Graph Data Understanding in Online Job Recommendations [63.19448893196642]
  We present a novel framework that harnesses the rich contextual information and semantic representations provided by large language models to analyze behavior graphs. By leveraging this capability, our framework enables personalized and accurate job recommendations for individual users.
  arXiv  Detail & Related papers  (2023-07-10T11:29:41Z)
• Recommender Systems in the Era of Large Language Models (LLMs) [62.0129013439038]
  Large Language Models (LLMs) have revolutionized the fields of Natural Language Processing (NLP) and Artificial Intelligence (AI) We conduct a comprehensive review of LLM-empowered recommender systems from various aspects including Pre-training, Fine-tuning, and Prompting.
  arXiv  Detail & Related papers  (2023-07-05T06:03:40Z)
• Just Fine-tune Twice: Selective Differential Privacy for Large Language Models [69.66654761324702]
  We propose a simple yet effective just-fine-tune-twice privacy mechanism to achieve SDP for large Transformer-based language models. Experiments show that our models achieve strong performance while staying robust to the canary insertion attack.
  arXiv  Detail & Related papers  (2022-04-15T22:36:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.