arXiv (All)
open access
Breaking News Out of the Filter Bubble: Generative AI Search Diversifies Collective Attention and Raises Shared Information Consumption
Generative AI search and AI overviews are transforming access to information and news, renewing concerns that readers will encounter a narrower range of topics and have less in common. We examine these concerns via a randomized field experiment with 37,561 readers at The Washington Post. Both groups
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- SCoNE: Selective Context-aware Neuron Editing for Robust Retrieval-Augmented Generation#820708
- Guiding the coarse levels of semantic IDs makes the fine levels learnable#1036910
- Optimizing document retrieval using massive text embeddings and LLM prompt engineering.#227806
- The search query filter bubble: effect of user ideology on political leaning of search results through query selection#73299
- Why shared attention vectors fail: a case for outcome-indexed tuning#985950
Record · ID 1143913
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive