Large Language Models, and LLM-Based Agents, Should Be Used to Enhance the Digital Public Sphere
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866916820386054144 |
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| author | Lazar, Seth Thorburn, Luke Jin, Tian Belli, Luca |
| author_facet | Lazar, Seth Thorburn, Luke Jin, Tian Belli, Luca |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2410_12123 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Large Language Models, and LLM-Based Agents, Should Be Used to Enhance the Digital Public Sphere Lazar, Seth Thorburn, Luke Jin, Tian Belli, Luca Computers and Society Information Retrieval K.4 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. |
| title | Large Language Models, and LLM-Based Agents, Should Be Used to Enhance the Digital Public Sphere |
| topic | Computers and Society Information Retrieval K.4 |
| url | https://arxiv.org/abs/2410.12123 |