A multi-LLM council scores predictive processing papers on an expert ontology, maps results in 3D hypothesis space, and introduces a dispersion metric showing greater spread in global versus local oddball paradigms.
Revealing hidden bias in ai: Lessons from large language models.arXiv preprint arXiv:2410.16927
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Empirical study across 10 tasks showing bias inheritance from LLM-augmented data harms related downstream performance, with three misalignment factors and three mitigation strategies identified.
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Ontology-constrained multi-LLM scoring of hypothesis support in the predictive processing literature
A multi-LLM council scores predictive processing papers on an expert ontology, maps results in 3D hypothesis space, and introduces a dispersion metric showing greater spread in global versus local oddball paradigms.
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Understanding and Mitigating Bias Inheritance in LLM-based Data Augmentation on Downstream Tasks
Empirical study across 10 tasks showing bias inheritance from LLM-augmented data harms related downstream performance, with three misalignment factors and three mitigation strategies identified.