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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2601.13433 |
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| _version_ | 1866916062880071680 |
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| author | Mammen, Priyanka Mary Joswin, Emil Venkitachalam, Shankar |
| author_facet | Mammen, Priyanka Mary Joswin, Emil Venkitachalam, Shankar |
| contents | 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 increasingly susceptible to incorrect/misleading endorsements as source expertise increases, with higher-authority sources inducing not only accuracy degradation but also increased confidence in wrong answers. We also show that this authority bias is mechanistically encoded within the model and a model can be steered away from the bias, thereby improving its performance even when an expert gives a misleading endorsement. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_13433 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Who Endorsed It? Measuring Authority Bias Across Expertise Levels in Language Models Mammen, Priyanka Mary Joswin, Emil Venkitachalam, Shankar Computation and Language Machine Learning 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 increasingly susceptible to incorrect/misleading endorsements as source expertise increases, with higher-authority sources inducing not only accuracy degradation but also increased confidence in wrong answers. We also show that this authority bias is mechanistically encoded within the model and a model can be steered away from the bias, thereby improving its performance even when an expert gives a misleading endorsement. |
| title | Who Endorsed It? Measuring Authority Bias Across Expertise Levels in Language Models |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2601.13433 |