Investigating the Impact of LLM Personality on Cognitive Bias Manifestation in Automated Decision-Making Tasks

• URL: http://arxiv.org/abs/2502.14219v1
• Date: Thu, 20 Feb 2025 03:15:54 GMT
• Title: Investigating the Impact of LLM Personality on Cognitive Bias Manifestation in Automated Decision-Making Tasks
• Authors: Jiangen He, Jiqun Liu,
• Abstract summary: Personality traits play a crucial role in either amplifying or reducing biases.<n>Conscientiousness and Agreeableness may generally enhance the efficacy of bias mitigation strategies.
• Score: 4.65004369765875
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are increasingly used in decision-making, yet their susceptibility to cognitive biases remains a pressing challenge. This study explores how personality traits influence these biases and evaluates the effectiveness of mitigation strategies across various model architectures. Our findings identify six prevalent cognitive biases, while the sunk cost and group attribution biases exhibit minimal impact. Personality traits play a crucial role in either amplifying or reducing biases, significantly affecting how LLMs respond to debiasing techniques. Notably, Conscientiousness and Agreeableness may generally enhance the efficacy of bias mitigation strategies, suggesting that LLMs exhibiting these traits are more receptive to corrective measures. These findings address the importance of personality-driven bias dynamics and highlight the need for targeted mitigation approaches to improve fairness and reliability in AI-assisted decision-making.

Related papers

• Assessing Judging Bias in Large Reasoning Models: An Empirical Study [99.86300466350013]
  Large Reasoning Models (LRMs) like DeepSeek-R1 and OpenAI-o1 have demonstrated remarkable reasoning capabilities. We present a benchmark comparing judging biases between LLMs and LRMs across both subjective preference-alignment datasets and objective fact-based datasets.
  arXiv  Detail & Related papers  (2025-04-14T07:14:27Z)
• Cognitive Debiasing Large Language Models for Decision-Making [71.2409973056137]
  Large language models (LLMs) have shown potential in supporting decision-making applications. We propose a cognitive debiasing approach, called self-debiasing, that enhances the reliability of LLMs. Our method follows three sequential steps -- bias determination, bias analysis, and cognitive debiasing -- to iteratively mitigate potential cognitive biases in prompts.
  arXiv  Detail & Related papers  (2025-04-05T11:23:05Z)
• 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)
• Persuasion with Large Language Models: a Survey [49.86930318312291]
  Large Language Models (LLMs) have created new disruptive possibilities for persuasive communication. In areas such as politics, marketing, public health, e-commerce, and charitable giving, such LLM Systems have already achieved human-level or even super-human persuasiveness. Our survey suggests that the current and future potential of LLM-based persuasion poses profound ethical and societal risks.
  arXiv  Detail & Related papers  (2024-11-11T10:05:52Z)
• 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)
• Towards Effective Evaluations and Comparisons for LLM Unlearning Methods [97.2995389188179]
  This paper seeks to refine the evaluation of machine unlearning for large language models. It addresses two key challenges -- the robustness of evaluation metrics and the trade-offs between competing goals.
  arXiv  Detail & Related papers  (2024-06-13T14:41:00Z)
• Evaluating Interventional Reasoning Capabilities of Large Language Models [58.52919374786108]
  Large language models (LLMs) are used to automate decision-making tasks.<n>In this paper, we evaluate whether LLMs can accurately update their knowledge of a data-generating process in response to an intervention.<n>We create benchmarks that span diverse causal graphs (e.g., confounding, mediation) and variable types.<n>These benchmarks allow us to isolate the ability of LLMs to accurately predict changes resulting from their ability to memorize facts or find other shortcuts.
  arXiv  Detail & Related papers  (2024-04-08T14:15:56Z)
• Towards detecting unanticipated bias in Large Language Models [1.4589372436314496]
  Large Language Models (LLMs) have exhibited fairness issues similar to those in previous machine learning systems. This research focuses on analyzing and quantifying these biases in training data and their impact on the decisions of these models.
  arXiv  Detail & Related papers  (2024-04-03T11:25:20Z)
• 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)
• MR-GSM8K: A Meta-Reasoning Benchmark for Large Language Model Evaluation [60.65820977963331]
  We introduce a novel evaluation paradigm for Large Language Models (LLMs) This paradigm shifts the emphasis from result-oriented assessments, which often neglect the reasoning process, to a more comprehensive evaluation. By applying this paradigm in the GSM8K dataset, we have developed the MR-GSM8K benchmark.
  arXiv  Detail & Related papers  (2023-12-28T15:49:43Z)
• 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)
• Delving into Identify-Emphasize Paradigm for Combating Unknown Bias [52.76758938921129]
  We propose an effective bias-conflicting scoring method (ECS) to boost the identification accuracy. We also propose gradient alignment (GA) to balance the contributions of the mined bias-aligned and bias-conflicting samples. Experiments are conducted on multiple datasets in various settings, demonstrating that the proposed solution can mitigate the impact of unknown biases.
  arXiv  Detail & Related papers  (2023-02-22T14:50:24Z)

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.