Problem
Research questions and friction points this paper is trying to address.
Authority Bias
Conversational Search Engines
Academic Paper Recommendation
Large Language Models
Authority Bias in Conversational Search Engines for Academic Paper Recommendation
🤖 AI Summary
研究探讨了学术论文推荐中对话搜索引擎的权威偏见问题，通过控制标题和摘要不变而改变权威元数据的方法，测试了不同大型语言模型中的权威偏见。
📝 Abstract
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, and citations rather than content. Holding title and abstract constant, we vary authority metadata across three counterfactual conditions (original, flipped, boosted) over eight LLMs (five open-weight and three frontier closed-weight) in an in-context, single-turn, top-1 recommendation setting. Our experiments show that authority bias is substantial and directional, varies markedly across models, and is only partially addressable through prompt-level debiasing. We further document a say-do gap: debiasing instructions suppress authority mentions far faster than authority-driven flips, so surface auditing systematically underestimates behavioral bias.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Authority Bias
Large Language Models
Causal Testing
Academic Paper Recommendation
Debiasing
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