Cover Image

Could you be wrong: Debiasing LLMs using a metacognitive prompt for improving human decision making

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Hills, Thomas T.
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Before you <think>, monitor: Implementing Flavell's metacognitive framework in LLMs
by: Oh, Nick
Published: (2025)

Biased AI improves human decision-making but reduces trust
by: Lai, Shiyang, et al.
Published: (2025)

Large language models for crowd decision making based on prompt design strategies using ChatGPT: models, analysis and challenges
by: Herrera-Poyatos, David, et al.
Published: (2024)

From DDMs to DNNs: Using process data and models of decision-making to improve human-AI interactions
by: Gopnarayan, Mrugsen Nagsen, et al.
Published: (2023)

Measuring the metacognition of AI
by: Servajean, Richard, et al.
Published: (2026)

It's the humans, not the data: Geopolitical bias in LLMs originates in post-training, amplified by the language of the prompt
by: Bladon, Stuart, et al.
Published: (2026)

Benchmarking the rationality of AI decision making using the transitivity axiom
by: Song, Kiwon, et al.
Published: (2025)

Industrial brain: a human-like autonomous neuro-symbolic cognitive decision-making system
by: Wang, Junping, et al.
Published: (2025)

TraveLLM: Could you plan my new public transit route in face of a network disruption?
by: Fang, Bowen, et al.
Published: (2024)

Confidence-weighted integration of human and machine judgments for superior decision-making
by: Yáñez, Felipe, et al.
Published: (2024)

Pitfalls of Conversational LLMs on News Debiasing
by: Schlicht, Ipek Baris, et al.
Published: (2024)

Can LLMs Grade Short-Answer Reading Comprehension Questions : An Empirical Study with a Novel Dataset
by: Henkel, Owen, et al.
Published: (2023)

Meta-learning ecological priors from large language models explains human learning and decision making
by: Jagadish, Akshay K., et al.
Published: (2025)

Efficient multi-prompt evaluation of LLMs
by: Polo, Felipe Maia, et al.
Published: (2024)

Social Debiasing for Fair Multi-modal LLMs
by: Cheng, Harry, et al.
Published: (2024)

Domain-level metacognitive monitoring in frontier LLMs: A 33-model atlas
by: Cacioli, Jon-Paul
Published: (2026)

Man and machine: artificial intelligence and judicial decision making
by: Dyevre, Arthur, et al.
Published: (2026)

Can Large Language Models Make the Grade? An Empirical Study Evaluating LLMs Ability to Mark Short Answer Questions in K-12 Education
by: Henkel, Owen, et al.
Published: (2024)

Could Chemical LLMs benefit from Message Passing
by: Xie, Jiaqing, et al.
Published: (2024)

When Life gives you LLMs, make LLM-ADE: Large Language Models with Adaptive Data Engineering
by: Choi, Stephen, et al.
Published: (2024)

Debiasing LLMs by Masking Unfairness-Driving Attention Heads
by: Han, Tingxu, et al.
Published: (2025)

Why should we ever automate moral decision making?
by: Conitzer, Vincent
Published: (2024)

Pedestrian crossing decisions can be explained by bounded optimal decision-making under noisy visual perception
by: Wang, Yueyang, et al.
Published: (2024)

AI reasoning effort predicts human decision time in content moderation
by: Davidson, Thomas
Published: (2025)

WorldLLM: Improving LLMs' world modeling using curiosity-driven theory-making
by: Levy, Guillaume, et al.
Published: (2025)

Beyond Nash Equilibrium: Bounded Rationality of LLMs and humans in Strategic Decision-making
by: Zheng, Kehan, et al.
Published: (2025)

Generative AI as a metacognitive agent: A comparative mixed-method study with human participants on ICF-mimicking exam performance
by: Pavlovic, Jelena, et al.
Published: (2024)

Unweighted ranking for value-based decision making with uncertainty
by: García, Aarón López, et al.
Published: (2026)

Detection of decision-making manipulation in the pairwise comparisons method
by: Strada, Michał, et al.
Published: (2024)

Agentic Misalignment: How LLMs Could Be Insider Threats
by: Lynch, Aengus, et al.
Published: (2025)

Design and testing of an agent chatbot supporting decision making with public transport data
by: Fantin, Luca, et al.
Published: (2025)

Debiasing Multilingual LLMs in Cross-lingual Latent Space
by: Peng, Qiwei, et al.
Published: (2025)

What you get is what you see: Decomposing Epistemic Planning using Functional STRIPS
by: Hu, Guang, et al.
Published: (2019)

Does AI help humans make better decisions? A statistical evaluation framework for experimental and observational studies
by: Ben-Michael, Eli, et al.
Published: (2024)

Could We Have Had Better Multilingual LLMs If English Was Not the Central Language?
by: Diandaru, Ryandito, et al.
Published: (2024)

Exploring EFL students' prompt engineering in human-AI story writing: an Activity Theory perspective
by: Woo, David James, et al.
Published: (2023)

A privacy-preserving distributed credible evidence fusion algorithm for collective decision-making
by: Ma, Chaoxiong, et al.
Published: (2024)

Modulation of temporal decision-making in a deep reinforcement learning agent under the dual-task paradigm
by: Pednekar, Amrapali, et al.
Published: (2025)

RobustDebias: Debiasing Language Models using Distributionally Robust Optimization
by: Gandhi, Deep, et al.
Published: (2026)

Beyond Hidden-Layer Manipulation: Semantically-Aware Logit Interventions for Debiasing LLMs
by: Xia, Wei
Published: (2025)