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Cognitive biases are systematic deviations in thinking that lead to irrational judgments and problematic decision-making, extensively studied across various fields.
Judgment under Uncertainty: Heuristics and Biases
Amos Tversky and Daniel Kahneman. 1974 · 1974
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Adding Asymmetrically Dominated Alternatives: Violations of Regularity and the Similarity Hypothesis
Joel Huber, John W. Payne, and Christopher Puto. 1982 · 1982
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Bias and error in human judgment
Arie W. Kruglanski and Icek Ajzen. 1983 · 1983
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Cognitive biases and their impact on strategic planning
James H. Barnes JR. 1984 · 1984
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Priming by pictures in lexical decision
Mary Vanderwart. 1984 · 1984
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Consequences of priming: Judgment and behavior
Paul M Herr. 1986 · 1986
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Priming and human memory systems
Endel Tulving and Daniel L Schacter. 1990 · 1990
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Anomalies: The endowment effect, loss aversion, and status quo bias
Daniel Kahneman, Jack L Knetsch, and Richard H Thaler. 1991 · 1991
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Loss Aversion in Riskless Choice: A Reference-Dependent Model
Amos Tversky and Daniel Kahneman. 1991 · 1991
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Advances in prospect theory: Cumulative representation of uncertainty
Amos Tversky and Daniel Kahneman. 1992 · 1992
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Overview of the TREC 2019 deep learning track
Nick Craswell, Bhaskar Mitra, Emine Yilmaz, Daniel Campos, and Ellen M. Voorhees. 2020 · 2003
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Do People Experience Cognitive Biases while Searching for Information?
Annie Y.S. Lau and Enrico W. Coiera. 2007 · 2007
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Toward a synthesis of cognitive biases: how noisy information processing can bias human decision making
Martin Hilbert. 2012 · 2012
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Reducing confirmation bias and evaluation bias: When are preference-inconsistent recommendations effective – and when not?
Christina Schwind and Jürgen Buder. 2012 · 2012
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Consumer health information searching process in real life settings
Yan Zhang. 2012 · 2012
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The Effect of Threshold Priming and Need for Cognition on Relevance Calibration and Assessment. In Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval (Dublin, Ireland) (SIGIR ’13) . Association for Computing Machinery, New York, NY, USA, 623–632
Falk Scholer, Diane Kelly, Wan-Ching Wu, Hanseul S. Lee, and William Webber. 2013 · 2013
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Captions and biases in diagnostic search
Ryen W. White and Eric Horvitz. 2013 · 2013
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Understanding and Predicting Graded Search Satisfaction. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining (Shanghai, China) (WSDM ’15) . Association for Computing Machinery, New York, NY, USA, 57–66
Jiepu Jiang, Ahmed Hassan Awadallah, Xiaolin Shi, and Ryen W. White. 2015 · 2015
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Confirmation Bias in Online Searches: Impacts of Selective Exposure Before an Election on Political Attitude Strength and Shifts
Silvia Knobloch-Westerwick, Benjamin K. Johnson, and Axel Westerwick. 2015 · 2015
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Anchoring and Adjustment in Relevance Estimation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (Santiago, Chile) (SIGIR ’15) . Association for Computing Machinery, New York, NY, USA, 963–966
Milad Shokouhi, Ryen White, and Emine Yilmaz. 2015 · 2015
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When does Relevance Mean Usefulness and User Satisfaction in Web Search?. In Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval (Pisa, Italy) (SIGIR ’16) . Association for Computing Machinery, New York, NY, USA, 463–472
Jiaxin Mao, Yiqun Liu, Ke Zhou, Jian-Yun Nie, Jingtao Song, Min Zhang, Shaoping Ma, Jiashen Sun, and Hengliang Luo. 2016 · 2016
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Quantifying Search Bias: Investigating Sources of Bias for Political Searches in Social Media. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (Portland, Oregon, USA) (CSCW ’17) . Association for Computing Machinery, New York, NY, USA, 417–432
Juhi Kulshrestha, Motahhare Eslami, Johnnatan Messias, Muhammad Bilal Zafar, Saptarshi Ghosh, Krishna P. Gummadi, and Karrie Karahalios. 2017 · 2017
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Scroll up or down? using wheel activity as an indicator of browsing strategy across different contextual factors. In Proceedings of the 2017 Conference on Conference Human Information Interaction and Retrieval . 333–336
Chang Liu, Jiqun Liu, and Yiming Wei. 2017 · 2017
Cited alongside, same era.
Understanding and Predicting Usefulness Judgment in Web Search. In Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (Shinjuku, Tokyo, Japan) (SIGIR ’17) . Association for Computing Machinery, New York, NY, USA, 1169–1172
Jiaxin Mao, Yiqun Liu, Huanbo Luan, Min Zhang, Shaoping Ma, Hengliang Luo, and Yuntao Zhang. 2017 · 2017
Cited alongside, same era.
Cognitive Biases in Crowdsourcing. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (Marina Del Rey, CA, USA) (WSDM ’18) . Association for Computing Machinery, New York, NY, USA, 162–170
Carsten Eickhoff. 2018 · 2018
Cited alongside, same era.
ChatGPT outperforms crowd workers for text-annotation tasks
Fabrizio Gilardi, Meysam Alizadeh, and Maël Kubli. 2023 · 2023
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Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Bias
Itay Itzhak, Gabriel Stanovsky, Nir Rosenfeld, and Yonatan Belinkov. 2023 · 2023
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A Behavioral Economics Approach to Interactive Information Retrieval: Understanding and Supporting Boundedly Rational Users . Vol. 48
Jiqun Liu. 2023 · 2023
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Constructing and meta-evaluating state-aware evaluation metrics for interactive search systems
Marco Markwald, Jiqun Liu, and Ran Yu. 2023 · 2023
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Llama 2: Open Foundation and Fine-Tuned Chat Models
Meta. 2023 · 2023
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"Satisfaction with Failure" or "Unsatisfied Success": Investigating the Relationship between Search Success and User Satisfaction. In Proceedings of the 2018 World Wide Web Conference (Lyon, France) (WWW ’18) . International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE, 1533–1542
Mengyang Liu, Yiqun Liu, Jiaxin Mao, Cheng Luo, Min Zhang, and Shaoping Ma. 2018 · 2018
Cited alongside, same era.
Empirical Evidence for Search Effectiveness Models
Alfan Farizki Wicaksono and Alistair Moffat. 2018 · 2018
Cited alongside, same era.
A Study of Immediate Requery Behavior in Search. In Proceedings of the 2018 Conference on Human Information Interaction & Retrieval (New Brunswick, NJ, USA) (CHIIR ’18) . Association for Computing Machinery, New York, NY, USA, 181–190
Haotian Zhang, Mustafa Abualsaud, and Mark D. Smucker. 2018 · 2018
Cited alongside, same era.
Investigating the impacts of expectation disconfirmation on web search. In Proceedings of the 2019 conference on human information interaction and retrieval . 319–323
Jiqun Liu and Chirag Shah. 2019 · 2019
Cited alongside, same era.
Investigating Cognitive Effects in Session-level Search User Satisfaction. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Anchorage, AK, USA, August 4-8, 2019 . 923–931
Mengyang Liu, Jiaxin Mao, Yiqun Liu, Min Zhang, and Shaoping Ma. 2019 · 2019
Cited alongside, same era.
Investigating Reference Dependence Effects on User Search Interaction and Satisfaction: A Behavioral Economics Perspective. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, China) (SIGIR ’20) . Association for Computing Machinery, New York, NY, USA, 1141–1150
Jiqun Liu and Fangyuan Han. 2020 · 2020
Cited alongside, same era.
Identifying and predicting the states of complex search tasks. In Proceedings of the 2020 conference on human information interaction and retrieval . 193–202
Jiqun Liu, Shawon Sarkar, and Chirag Shah. 2020 · 2020
Cited alongside, same era.
Cascade or Recency: Constructing Better Evaluation Metrics for Session Search. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (Virtual Event, China) (SIGIR ’20) . Association for Computing Machinery, New York, NY, USA, 389–398
Fan Zhang, Jiaxin Mao, Yiqun Liu, Weizhi Ma, Min Zhang, and Shaoping Ma. 2020 · 2020
Cited alongside, same era.
Persistent Anti-Muslim Bias in Large Language Models. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (Virtual Event, USA) (AIES ’21) . Association for Computing Machinery, New York, NY, USA, 298–306
Abubakar Abid, Maheen Farooqi, and James Zou. 2021 · 2021
Cited alongside, same era.
Large language models propagate race-based medicine
Jesutofunmi A Omiye, Jenna C Lester, Simon Spichak, Veronica Rotemberg, and Roxana Daneshjou. 2023 · 2023
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Supporting Human-AI Collaboration in Auditing LLMs with LLMs. In Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society (Montréal, QC, Canada) (AIES ’23) . Association for Computing Machinery, New York, NY, USA, 913–926
Charvi Rastogi, Marco Tulio Ribeiro, Nicholas King, Harsha Nori, and Saleema Amershi. 2023 · 2023
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The persistence of cognitive biases in financial decisions across economic groups
Kai Ruggeri, Sarah Ashcroft-Jones, Giampaolo Abate Romero Landini, Narjes Al-Zahli, Natalia Alexander, Mathias Houe Andersen, Katherine Bibilouri, Katharina Busch, Valentina Cafarelli, Jennifer Chen, et al · 2023
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Understanding and Predicting User Satisfaction with Conversational Recommender Systems
Clemencia Siro, Mohammad Aliannejadi, and Maarten De Rijke. 2023 · 2023
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Large language models can accurately predict searcher preferences
Paul Thomas, Seth Spielman, Nick Craswell, and Bhaskar Mitra. 2023 · 2023
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Investigating the role of in-situ user expectations in Web search
Ben Wang and Jiqun Liu. 2023 · 2023
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Search under Uncertainty: Cognitive Biases and Heuristics-Tutorial on Modeling Search Interaction using Behavioral Economics. In Proceedings of the 2024 Conference on Human Information Interaction and Retrieval . 427–430
Leif Azzopardi and Jiqun Liu. 2024 · 2024
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Measuring Political Bias in Large Language Models: What Is Said and How It Is Said
Yejin Bang, Delong Chen, Nayeon Lee, and Pascale Fung. 2024 · 2024
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Decoy Effect In Search Interaction: Understanding User Behavior and Measuring System Vulnerability
Nuo Chen, Jiqun Liu, Hanpei Fang, Yuankai Luo, Tetsuya Sakai, and Xiao-Ming Wu. 2024 · 2024
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Cognitive Bias in High-Stakes Decision-Making with LLMs
Jessica Maria Echterhoff, Yao Liu, Abeer Alessa, Julian J. McAuley, and Zexue He. 2024 · 2024
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Reducing Selection Bias in Large Language Models
J. E. Eicher and R. F. Irgolič. 2024 · 2024
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Search under uncertainty: Cognitive biases and heuristics: a tutorial on testing, mitigating and accounting for cognitive biases in search experiments. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval . 3013–3016
Jiqun Liu and Leif Azzopardi. 2024 · 2024
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Addressing cognitive bias in medical language models
Samuel Schmidgall, Carl Harris, Ime Essien, Daniel Olshvang, Tawsifur Rahman, Ji Woong Kim, Rojin Ziaei, Jason Eshraghian, Peter Abadir, and Rama Chellappa. 2024 · 2024
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UMBRELA: UMbrela is the (Open-Source Reproduction of the) Bing RELevance Assessor
Shivani Upadhyay, Ronak Pradeep, Nandan Thakur, Nick Craswell, and Jimmy Lin. 2024 · 2024
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Understanding users’ dynamic perceptions of search gain and cost in sessions: An expectation confirmation model
Ben Wang and Jiqun Liu. 2024 · 2024
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A Comparative Study of Explicit and Implicit Gender Biases in Large Language Models via Self-evaluation. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC/COLING 2024, 20-25 May, 2024, Torino, Italy , Nicoletta Calzolari, Min-Yen Kan, Véronique Hoste, Alessandro Lenci, Sakriani Sakti, and Nianwen Xue (Eds.). ELRA and ICCL, 186–198
Yachao Zhao, Bo Wang, Yan Wang, Dongming Zhao, Xiaojia Jin, Jijun Zhang, Ruifang He, and Yuexian Hou. 2024 · 2024
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