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The Hidden Frame: How Large Language Models Impact Democratic Society — arXiv:2609.07735

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.07735
License (as archived): CC0-1.0
Archived: 2026-10-16T12:45:45.855Z
SHA-256 of the archived text: ed33e7369deb58f4d876d62ca9d36caaafddd5d6cc537ee1bce698a6206593ba
Stable URL of this page: https://metraipolis.com/documento/dc_614c340db697

How to cite

The Hidden Frame: How Large Language Models Impact Democratic Society — arXiv:2609.07735 — arXiv (en), https://arxiv.org/abs/2609.07735 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_614c340db697 (Metraipolis archive dc_614c340db697, sha256 ed33e7369deb58f4d876d62ca9d36caaafddd5d6cc537ee1bce698a6206593ba)

BibTeX

@misc{dc614c340db697,
  title = {The Hidden Frame: How Large Language Models Impact Democratic Society — arXiv:2609.07735},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.07735},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_614c340db697; sha256 ed33e7369deb58f4d876d62ca9d36caaafddd5d6cc537ee1bce698a6206593ba},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.07735. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language models are rapidly becoming an interface between citizens and political information. They are often regarded as "a better Google." While this analogy might work for some instances, it is unintuitively problematic for democratic politics. A search engine retrieves human-authored documents, while a language model generates novel text that necessarily embeds invisible framing decisions. Because conveying knowledge involves framing, a system that generates answers cannot serve as a neutral conduit to "all human knowledge." Instead, these systems are becoming a new kind of political intermediary. Mechanistic evidence shows that partisan identity is encoded as a locatable geometric direction inside the Llama 3.1 8B model, and that alignment training masks rather than removes this structure. Building on that evidence, we use the model's training cutoff in 2023 to make those frames visible. This cutpoint auspiciously falls just before a dramatic realignment in American politics marked by the second Trump administration and the MAHA transformation of health politics, providing us with a natural experiment. We find that the model presents temporally contingent partisan alignments as {\em knowledge}, with no reliable mechanism for distinguishing fact from opinion. Because citizens judge political claims by who made them and when, an answer that carries neither cue hampers the judgment on which self-governance depends. Large language models purporting to summarize "all human knowledge" are, in actuality, simply magnifying the cultural and partisan divides inherent in their training data.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-09-09 in cs.CL).
Categorie arXiv: Computation and Language (cs.CL).
Pagina del lavoro: https://arxiv.org/abs/2609.07735 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-16T12:30:25.137Z dalla pagina https://arxiv.org/catchup/cs.CL/2026-09-09?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).