arXiv:2601.13433v3 Announce Type: replace-cross Abstract: Prior research demonstrates that performance of language models on reasoning tasks can be influenced by suggestions, hints and endorsements. However, the influence of endorsement source credibility remains underexplored. We investigate whether language models exhibit systematic bias based on the perceived expertise of the provider of the endorsement. Across 4 datasets spanning mathematical, legal, and medical reasoning, we evaluate 11 models using personas representing four expertise levels per domain. Our results reveal that models are
The rapid advancement and deployment of large language models make understanding and mitigating their inherent biases, such as authority bias, crucial for reliable integration into critical applications.
Understanding how LLMs are influenced by perceived expertise provides critical insights into their decision-making processes, directly impacting their trustworthiness and potential for manipulation across sensitive domains.
We now have clearer empirical evidence that LLMs exhibit systematic biases based on the source of endorsement, suggesting a need for more robust training and ethical guidelines to counteract such influences.
- · AI ethics researchers
- · Organizations developing responsible AI
- · End-users of robust, unbiased AI systems
- · Developers ignoring bias mitigation
- · AI systems prone to subtle manipulation
- · Applications requiring absolute neutrality
More sophisticated methods for auditing and debiasing LLMs will be developed and implemented across various industries.
Public and regulatory scrutiny on AI transparency and bias mitigation will intensify, potentially leading to new compliance standards.
The development of 'expert-agnostic' or 'trust-resistant' AI architectures could emerge as a new research frontier, fundamentally altering how LLMs process information.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG