用大模型推荐系统重塑网络信息分发，让个人掌控注意力
Large Language Models, and LLM-Based Agents, Should Be Used to Enhance the Digital Public Sphere
- 用大模型理解用户自然语言目标，匹配深层偏好
- 无需集中数据即可生成推荐，避免隐私滥用
- 适合关注数字公平、注意力主权的学者与政策制定者
本文主张，基于大语言模型的推荐系统可取代当前以注意力为导向的信息分发机制。此类系统将读取开放网络内容，推断用户的自然语言目标，并呈现符合其反思性偏好的信息。设计得当，可实现个性化推荐而无需大规模数据囤积，恢复个体控制权，优化真实需求而非点击率等代理指标，并支持自主注意力管理。结合现有系统危害证据与近期大模型驱动流程的研究，本文识别出四项关键挑战：在无中心化数据前提下生成候选内容、保持计算效率、稳健建模用户偏好，以及抵御提示注入攻击。这些问题均非不可逾越，克服后将推动数字公共领域向民主化、以人为本的价值方向演进。
原文摘要 · Abstract (English)
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.
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