arXiv AI

Authority Bias in Conversational Search Engines for Academic Paper Recommendation

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.

Hugging Face Trending Papers

Jun 11

Authority, Truth, and Citation Bias: A Large-Scale Multi-Domain Benchmark for Studying Epistemic Susceptibility in Large Language Models

Large language models are increasingly deployed in citation-augmented settings, yet the effect of citation presence on model behavior independent of factual content remains poorly understood. We introduce AuthorityBench, a 220,564-prompt multi-domain benchmark that isolates how citation-based authority signals influence epistemic behavior in LLMs.

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arXiv AI

Aug 25

On the Role of Citations in Preference Data

arXiv:2608.21376v1 Announce Type: cross Abstract: Many NLP tasks require systems to provide attribution in their outputs--i.e. citations to grounding sources. Attribution serves as a bulwark against...

By Yu Hou, Hal Daum\'e III, Rachel Rudinger, William Walden

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arXiv Computation and Language

Sep 1

Answer Bubbles: Information Exposure in AI-Mediated Search

arXiv:2603.16138v2 Announce Type: replace-cross Abstract: Generative search systems are increasingly replacing link-based retrieval with AI-generated summaries, yet little is known about how these sy...

By Michelle Huang, Agam Goyal, Koustuv Saha, Eshwar Chandrasekharan

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arXiv Computation and Language

Sep 2

Citing Less Critically: LLMs Reshape the Rhetoric and Reach of Scientific Citation

arXiv:2609.01432v1 Announce Type: cross Abstract: Scientific citations carry rhetorical intent. Scholars may cite prior work positively (supporting), negatively (contrasting), or neutrally (mentionin...

By Yixuan Liu, Lin Chen, Zhuoqi Liu, Jianglin Lu, Dakota Murray

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arXiv Machine Learning

Aug 4

Computational Approaches to Understanding Large Language Model Impact on Writing and Information Ecosystems

arXiv:2506. 17467v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have shown significant potential to change how we write, communicate, and create, leading to rapid adoption across society.

By Weixin Liang

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arXiv AI

Oct 5

Measuring Google AI Overviews: Activation, Source Quality, Claim Fidelity, and Publisher Impact

The study measures Google AI Overviews (AIOs), noting they activate for 13.7% of queries overall and 64.7% of question-form queries, with lower rates for politically sensitive topics. AIOs cite more credible domains than typical search results, yet 30% of cited pages are not shown in the first-page results, indicating a distinct source selection process. Among 98,020 atomic claims, 11.0% lack support from cited pages, mainly due to omissions, and over half of cited pages contain display ads, reducing publisher revenue while Google’s ads remain.

By Haofei Xu, Umar Iqbal, Jacob M. Montgomery

arXiv AI

Aug 7

Large Language Models Threaten Double-blind Review

arXiv:2608. 05157v1 Announce Type: cross Abstract: Double blind peer review serves as the scientific community primary defense against status and affiliation bias.

By Bulambo Mwendelwa Gloire, Prasenjit Mitra

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arXiv AI

Oct 1

Search Shapes Conclusions: Auditing Evidence Selection Bias in Deep Research Agents

arXiv:2609.39026v1 Announce Type: new Abstract: Deep Research agents synthesize evidence into cited reports, yet a well-cited report can still reach a misleading conclusion. Citation correctness chec...

By Shuyao Xiao, Shengling Wang, Xuan Chen, Ke Chao, Ming Cui, Feifei Qian, Chaoyang Mei, Fanlin Meng, Lulu Wang, Ziming Yu, Junxi Yin

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arXiv AI

Aug 20

Self-prompting and cross-model consensus enable reproducible data extraction from scientific literature with large language models

The paper investigates how large language models can extract contextualized data from scientific literature. It presents four workflows: expert‑written prompts, self‑generated prompts, autonomous literature discovery, and dataset creation from guidelines. While models perform well with prompts, they struggle with context, hallucinate references, and still need human oversight for final validation.

By Valentin Romanov, Monique Bax, Steven Niederer

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arXiv AI

Sep 28

Attribution Bias in Large Language Models

The paper introduces AttriBench, a benchmark dataset that balances author fame and demographics to study quote attribution in large language models (LLMs). Using AttriBench, the authors evaluate 11 popular LLMs and find that accurate attribution remains difficult, with significant disparities across race, gender, and intersectional groups. They also identify a new failure mode—suppression—where models omit attribution entirely, which is unevenly distributed across demographics and not reflected by standard accuracy metrics.

By Eliza Berman, Bella Chang, Daniel B. Neill, Emily Black

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arXiv AI

Jun 3

LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems

arXiv:2606. 02883v1 Announce Type: cross Abstract: Recommender systems have grown from content-organization tools into sophisticated systems that shape daily behavior.

By Amir Ghasemian, Homa Hosseinmardi, Upasana Dutta, Duncan J. Watts

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arXiv Computation and Language

Sep 3

Are Non-English Papers Reviewed Fairly? Language-of-Study Bias in NLP Peer Reviews

The paper investigates language-of-study (LoS) bias in NLP peer reviews, defining and distinguishing negative and positive forms of bias. Using a new dataset, LOBSTER, and an LLM-based detection pipeline, the authors analyze 15,645 reviews and find that non‑English papers experience significantly higher bias rates, with negative bias outweighing positive bias. They further identify four subcategories of negative bias, noting that demanding unjustified cross‑lingual generalization is the most common.

By Ehsan Barkhordar, Abdulfattah Safa, Verena Blaschke, Erika Lombart, Marie-Catherine de Marneffe, G\"ozde G\"ul \c{S}ahin

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