@inproceedings{jinadu-etal-2026-authority,
title = "Authority Bias in Conversational Search Engines for Academic Paper Recommendation.",
author = "Jinadu, Uthman and
Ghazvinian, Parsa and
Budathoki, Anjila and
Ampel, Benjamin M. and
Sunderraman, Rajshekhar and
Ding, Yi",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.emnlp-main.1069/",
pages = "24548--24568",
ISBN = "979-8-89176-459-0",
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 \textit{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.\par Code: https://github.com/jinaduuthman/Authority-Bias-In-Conversational-Search-Engine\par Data: https://huggingface.co/datasets/uthmanjinadu/authority-bias-paper-recommendation"
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="jinadu-etal-2026-authority">
<titleInfo>
<title>Authority Bias in Conversational Search Engines for Academic Paper Recommendation.</title>
</titleInfo>
<name type="personal">
<namePart type="given">Uthman</namePart>
<namePart type="family">Jinadu</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Parsa</namePart>
<namePart type="family">Ghazvinian</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Anjila</namePart>
<namePart type="family">Budathoki</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Benjamin</namePart>
<namePart type="given">M</namePart>
<namePart type="family">Ampel</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Rajshekhar</namePart>
<namePart type="family">Sunderraman</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Yi</namePart>
<namePart type="family">Ding</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-10</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing</title>
</titleInfo>
<name type="personal">
<namePart type="given">Hung-Yi</namePart>
<namePart type="family">Lee</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sunipa</namePart>
<namePart type="family">Dev</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jessy</namePart>
<namePart type="family">Li</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Desmond</namePart>
<namePart type="family">Elliott</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Budapest, Hungary</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
<identifier type="isbn">979-8-89176-459-0</identifier>
</relatedItem>
<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.\par Code: https://github.com/jinaduuthman/Authority-Bias-In-Conversational-Search-Engine\par Data: https://huggingface.co/datasets/uthmanjinadu/authority-bias-paper-recommendation</abstract>
<identifier type="citekey">jinadu-etal-2026-authority</identifier>
<location>
<url>https://aclanthology.org/2026.emnlp-main.1069/</url>
</location>
<part>
<date>2026-10</date>
<extent unit="page">
<start>24548</start>
<end>24568</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Authority Bias in Conversational Search Engines for Academic Paper Recommendation.
%A Jinadu, Uthman
%A Ghazvinian, Parsa
%A Budathoki, Anjila
%A Ampel, Benjamin M.
%A Sunderraman, Rajshekhar
%A Ding, Yi
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F jinadu-etal-2026-authority
%X 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.\par Code: https://github.com/jinaduuthman/Authority-Bias-In-Conversational-Search-Engine\par Data: https://huggingface.co/datasets/uthmanjinadu/authority-bias-paper-recommendation
%U https://aclanthology.org/2026.emnlp-main.1069/
%P 24548-24568
Markdown (Informal)
[Authority Bias in Conversational Search Engines for Academic Paper Recommendation.](https://aclanthology.org/2026.emnlp-main.1069/) (Jinadu et al., EMNLP 2026)
ACL