Cognitive Debiasing Large Language Models for Decision-Making

• URL: http://arxiv.org/abs/2504.04141v2
• Date: Thu, 10 Apr 2025 04:45:38 GMT
• Title: Cognitive Debiasing Large Language Models for Decision-Making
• Authors: Yougang Lyu, Shijie Ren, Yue Feng, Zihan Wang, Zhumin Chen, Zhaochun Ren, Maarten de Rijke,
• Abstract summary: Large language models (LLMs) have shown potential in supporting decision-making applications.<n>We propose a cognitive debiasing approach, called self-debiasing, that enhances the reliability of LLMs.<n>Our method follows three sequential steps -- bias determination, bias analysis, and cognitive debiasing -- to iteratively mitigate potential cognitive biases in prompts.
• Score: 71.2409973056137
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large language models (LLMs) have shown potential in supporting decision-making applications, particularly as personal conversational assistants in the financial, healthcare, and legal domains. While prompt engineering strategies have enhanced the capabilities of LLMs in decision-making, cognitive biases inherent to LLMs present significant challenges. Cognitive biases are systematic patterns of deviation from norms or rationality in decision-making that can lead to the production of inaccurate outputs. Existing cognitive bias mitigation strategies assume that input prompts contain (exactly) one type of cognitive bias and therefore fail to perform well in realistic settings where there maybe any number of biases. To fill this gap, we propose a cognitive debiasing approach, called self-debiasing, that enhances the reliability of LLMs by iteratively refining prompts. Our method follows three sequential steps -- bias determination, bias analysis, and cognitive debiasing -- to iteratively mitigate potential cognitive biases in prompts. Experimental results on finance, healthcare, and legal decision-making tasks, using both closed-source and open-source LLMs, demonstrate that the proposed self-debiasing method outperforms both advanced prompt engineering methods and existing cognitive debiasing techniques in average accuracy under no-bias, single-bias, and multi-bias settings.

Related papers

• Cognitive Bias Detection Using Advanced Prompt Engineering [0.0]
  This paper introduces a novel approach for real-time cognitive bias detection in user-generated text using large language models (LLMs) and advanced prompt engineering techniques. The proposed system analyzes textual data to identify common cognitive biases such as confirmation bias, circular reasoning, and hidden assumption.
  arXiv  Detail & Related papers  (2025-03-07T15:35:37Z)
• Cognitive Biases in Large Language Models for News Recommendation [68.90354828533535]
  This paper explores the potential impact of cognitive biases on large language models (LLMs) based news recommender systems. We discuss strategies to mitigate these biases through data augmentation, prompt engineering and learning algorithms aspects.
  arXiv  Detail & Related papers  (2024-10-03T18:42:07Z)
• Identifying and Mitigating Social Bias Knowledge in Language Models [52.52955281662332]
  We propose a novel debiasing approach, Fairness Stamp (FAST), which enables fine-grained calibration of individual social biases.<n>FAST surpasses state-of-the-art baselines with superior debiasing performance.<n>This highlights the potential of fine-grained debiasing strategies to achieve fairness in large language models.
  arXiv  Detail & Related papers  (2024-08-07T17:14:58Z)
• The African Woman is Rhythmic and Soulful: An Investigation of Implicit Biases in LLM Open-ended Text Generation [3.9945212716333063]
  Implicit biases are significant because they influence the decisions made by Large Language Models (LLMs) Traditionally, explicit bias tests or embedding-based methods are employed to detect bias, but these approaches can overlook more nuanced, implicit forms of bias. We introduce two novel psychological-inspired methodologies to reveal and measure implicit biases through prompt-based and decision-making tasks.
  arXiv  Detail & Related papers  (2024-07-01T13:21:33Z)
• Balancing Rigor and Utility: Mitigating Cognitive Biases in Large Language Models for Multiple-Choice Questions [0.46873264197900916]
  This paper examines the role of cognitive biases in the decision-making processes of large language models (LLMs) We show that certain cognitive biases when properly balanced, can enhance decision-making efficiency through rational deviations and shortcuts.
  arXiv  Detail & Related papers  (2024-06-16T16:25:22Z)
• Prompting Fairness: Integrating Causality to Debias Large Language Models [19.76215433424235]
  Large language models (LLMs) are susceptible to generating biased and discriminatory responses. We propose a causality-guided debiasing framework to tackle social biases.
  arXiv  Detail & Related papers  (2024-03-13T17:46:28Z)
• Cognitive Bias in Decision-Making with LLMs [19.87475562475802]
  Large language models (LLMs) offer significant potential as tools to support an expanding range of decision-making tasks. LLMs have been shown to inherit societal biases against protected groups, as well as be subject to bias functionally resembling cognitive bias. Our work introduces BiasBuster, a framework designed to uncover, evaluate, and mitigate cognitive bias in LLMs.
  arXiv  Detail & Related papers  (2024-02-25T02:35:56Z)
• Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes [73.12947922129261]
  We leverage the zero-shot capabilities of large language models to reduce stereotyping. We show that self-debiasing can significantly reduce the degree of stereotyping across nine different social groups. We hope this work opens inquiry into other zero-shot techniques for bias mitigation.
  arXiv  Detail & Related papers  (2024-02-03T01:40:11Z)
• Exploring the Jungle of Bias: Political Bias Attribution in Language Models via Dependency Analysis [86.49858739347412]
  Large Language Models (LLMs) have sparked intense debate regarding the prevalence of bias in these models and its mitigation. We propose a prompt-based method for the extraction of confounding and mediating attributes which contribute to the decision process. We find that the observed disparate treatment can at least in part be attributed to confounding and mitigating attributes and model misalignment.
  arXiv  Detail & Related papers  (2023-11-15T00:02:25Z)
• Leveraging Expert Consistency to Improve Algorithmic Decision Support [62.61153549123407]
  We explore the use of historical expert decisions as a rich source of information that can be combined with observed outcomes to narrow the construct gap. We propose an influence function-based methodology to estimate expert consistency indirectly when each case in the data is assessed by a single expert. Our empirical evaluation, using simulations in a clinical setting and real-world data from the child welfare domain, indicates that the proposed approach successfully narrows the construct gap.
  arXiv  Detail & Related papers  (2021-01-24T05:40:29Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.