Authority Bias in Conversational Search Engines for Academic Paper Recommendation
Read the original on arXiv AI →The study examines whether large language models (LLMs) used as conversational search engines for academic literature prioritize papers based on authority signals—such as author prestige, venue, and citations—rather than content. By keeping titles and abstracts constant and manipulating authority metadata across three counterfactual conditions (original, flipped, boosted), the researchers tested eight LLMs in a single-turn, top‑1 recommendation scenario. Results reveal a substantial, directional authority bias that varies across models and is only partially mitigated by prompt-level debiasing, while also highlighting a significant say‑do gap where debiasing instructions suppress authority mentions more quickly than authority-driven flips, leading to underestimation of behavioral bias.
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