Buchumschlag

Balancing Rigor and Utility: Mitigating Cognitive Biases in Large Language Models for Multiple-Choice Questions

Fuente: arXiv

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Zhong, Hanyang, Wang, Liman, Cao, Wenting, Sun, Zeyuan
Format:	Preprint
Veröffentlicht:	2024
Schlagworte:	Computation and Language Artificial Intelligence
Online-Zugang:	Acceder al recurso
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Ähnliche Einträge

LLM-SAP: Large Language Models Situational Awareness Based Planning
von: Wang, Liman, et al.
Veröffentlicht: (2023)

Cognitive Biases in Large Language Models: A Survey and Mitigation Experiments
von: Sumita, Yasuaki, et al.
Veröffentlicht: (2024)

Cognitively Diverse Multiple-Choice Question Generation: A Hybrid Multi-Agent Framework with Large Language Models
von: Tian, Yu, et al.
Veröffentlicht: (2026)

Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models
von: Bao, Qiming, et al.
Veröffentlicht: (2023)

Multiple-Choice Question Generation Using Large Language Models: Methodology and Educator Insights
von: Biancini, Giorgio, et al.
Veröffentlicht: (2025)

Differentiating Choices via Commonality for Multiple-Choice Question Answering
von: Deng, Wenqing, et al.
Veröffentlicht: (2024)

FENet: Focusing Enhanced Network for Lane Detection
von: Wang, Liman, et al.
Veröffentlicht: (2023)

A Study on Large Language Models' Limitations in Multiple-Choice Question Answering
von: Khatun, Aisha, et al.
Veröffentlicht: (2024)

Large Language Models for Multi-Choice Question Classification of Medical Subjects
von: Ponce-López, Víctor
Veröffentlicht: (2024)

Self-Correcting Large Language Models: Generation vs. Multiple Choice
von: Rahmani, Hossein A., et al.
Veröffentlicht: (2025)

Multiple Choice Questions: Reasoning Makes Large Language Models (LLMs) More Self-Confident, Especially When They are Wrong
von: Fu, Tairan, et al.
Veröffentlicht: (2025)

(Ir)rationality and Cognitive Biases in Large Language Models
von: Macmillan-Scott, Olivia, et al.
Veröffentlicht: (2024)

Intent-Aware Self-Correction for Mitigating Social Biases in Large Language Models
von: Anantaprayoon, Panatchakorn, et al.
Veröffentlicht: (2025)

Cognitive Biases in Large Language Models for News Recommendation
von: Lyu, Yougang, et al.
Veröffentlicht: (2024)

Question Difficulty Ranking for Multiple-Choice Reading Comprehension
von: Raina, Vatsal, et al.
Veröffentlicht: (2024)

Mitigating Social Biases in Language Models through Unlearning
von: Dige, Omkar, et al.
Veröffentlicht: (2024)

Mitigating Knowledge Conflicts in Language Model-Driven Question Answering
von: Cao, Han, et al.
Veröffentlicht: (2024)

Generating Multiple-Choice Knowledge Questions with Interpretable Difficulty Estimation using Knowledge Graphs and Large Language Models
von: Şakiroğlu, Mehmet Can, et al.
Veröffentlicht: (2026)

Plausibly Problematic Questions in Multiple-Choice Benchmarks for Commonsense Reasoning
von: Palta, Shramay, et al.
Veröffentlicht: (2024)

Large Language Models are Biased Because They Are Large Language Models
von: Resnik, Philip
Veröffentlicht: (2024)

Self-Blinding and Counterfactual Self-Simulation Mitigate Biases and Sycophancy in Large Language Models
von: Christian, Brian, et al.
Veröffentlicht: (2026)

Collaboration among Multiple Large Language Models for Medical Question Answering
von: Shang, Kexin, et al.
Veröffentlicht: (2025)

Automated Generation and Tagging of Knowledge Components from Multiple-Choice Questions
von: Moore, Steven, et al.
Veröffentlicht: (2024)

Bias Vector: Mitigating Biases in Language Models with Task Arithmetic Approach
von: Shirafuji, Daiki, et al.
Veröffentlicht: (2024)

Generating Plausible Distractors for Multiple-Choice Questions via Student Choice Prediction
von: Lee, Yooseop, et al.
Veröffentlicht: (2025)

Answer, Assemble, Ace: Understanding How LMs Answer Multiple Choice Questions
von: Wiegreffe, Sarah, et al.
Veröffentlicht: (2024)

Automated Generation of Challenging Multiple-Choice Questions for Vision Language Model Evaluation
von: Zhang, Yuhui, et al.
Veröffentlicht: (2025)

SQ-LLaVA: Self-Questioning for Large Vision-Language Assistant
von: Sun, Guohao, et al.
Veröffentlicht: (2024)

Benchmarking Cognitive Biases in Large Language Models as Evaluators
von: Koo, Ryan, et al.
Veröffentlicht: (2023)

Biomedical Entity Linking as Multiple Choice Question Answering
von: Lin, Zhenxi, et al.
Veröffentlicht: (2024)

Option-ID Based Elimination For Multiple Choice Questions
von: Zhu, Zhenhao, et al.
Veröffentlicht: (2025)

FairPy: A Toolkit for Evaluation of Prediction Biases and their Mitigation in Large Language Models
von: Viswanath, Hrishikesh, et al.
Veröffentlicht: (2023)

Locating and Mitigating Gender Bias in Large Language Models
von: Cai, Yuchen, et al.
Veröffentlicht: (2024)

CURATRON: Complete and Robust Preference Data for Rigorous Alignment of Large Language Models
von: Nguyen, Son The, et al.
Veröffentlicht: (2024)

Multilingual Performance Biases of Large Language Models in Education
von: Gupta, Vansh, et al.
Veröffentlicht: (2025)

Response Generation for Cognitive Behavioral Therapy with Large Language Models: Comparative Study with Socratic Questioning
von: Izumi, Kenta, et al.
Veröffentlicht: (2024)

Conformal P-Value in Multiple-Choice Question Answering Tasks with Provable Risk Control
von: Ye, Yuanchang
Veröffentlicht: (2025)

From Biased Chatbots to Biased Agents: Examining Role Assignment Effects on LLM Agent Robustness
von: Cao, Linbo, et al.
Veröffentlicht: (2026)

MLLM-Fabric: Multimodal Large Language Model-Driven Robotic Framework for Fabric Sorting and Selection
von: Wang, Liman, et al.
Veröffentlicht: (2025)

Cannot See the Forest for the Trees: Invoking Heuristics and Biases to Elicit Irrational Choices of LLMs
von: Yang, Haoming, et al.
Veröffentlicht: (2025)