AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming
in LLM-Based Batch Relevance Assessment

AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessment

24 September 2024

Nuo Chen

Tetsuya Sakai

Xiao-Ming Wu

Papers citing "AI Can Be Cognitively Biased: An Exploratory Study on Threshold Priming in LLM-Based Batch Relevance Assessment"

6 / 6 papers shown

Title
Cognitive Debiasing Large Language Models for Decision-Making Yougang Lyu Shijie Ren Yue Feng Zihan Wang Z. Chen Z. Z. Ren Maarten de Rijke 38 0 0 05 Apr 2025
Trapped by Expectations: Functional Fixedness in LLM-Enabled Chat Search Jiqun Liu Jamshed Karimnazarov Ryen W. White 36 0 0 02 Apr 2025
LLM-Driven Usefulness Labeling for IR Evaluation Mouly Dewan Jiqun Liu Chirag Shah 58 0 0 13 Mar 2025
The Decoy Dilemma in Online Medical Information Evaluation: A Comparative Study of Credibility Assessments by LLM and Human Judges Jiqun Liu Jiangen He 78 2 0 23 Nov 2024
Mitigating Selection Bias with Node Pruning and Auxiliary Options Hyeong Kyu Choi Weijie Xu Chi Xue Stephanie Eckman Chandan K. Reddy 31 1 0 27 Sep 2024
Query Performance Prediction using Relevance Judgments Generated by Large Language Models Chuan Meng Negar Arabzadeh Arian Askari Mohammad Aliannejadi Maarten de Rijke LRM 37 11 0 01 Apr 2024