arXiv (All)
open access
Authority Bias in Conversational Search Engines for Academic Paper Recommendation
Large Language Models (LLMs) are increasingly used as conversational search engines for academic literature, yet whether they judge papers on content or on authority signals has not been tested causally. We investigate authority bias: systematic preference for papers based on author prestige, venue,
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
- Authority Bias in Conversational Search Engines for Academic Paper Recommendation#809710
- Editorial routing shapes how computational results are qualified in AI-assisted scientific writing#1083741
- Informational Help-Seeking on Reddit Did Not Decline After ChatGPT#1000395
- How People Use ChatGPT: Conversation-Level Evidence from India, Nigeria, Brazil, and Pakistan#1142880
- Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy#199401
Record · ID 819706
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