arXiv (All)
open access
BTBR: A Bayesian-Theory-Driven Probabilistic-Fuzzy Framework for Implicit Bias Removal in Large Language Models
Large language models (LLMs) may encode biased associations from heterogeneous training corpora that are not immediately visible under ordinary prompting, but can surface when the model is steered toward particular demographic personas. Such behavior often manifests not as explicit toxic output, but
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Acmite: Mitigating Gender Bias in LLMs through Concept-Guided Mutual Information#1156967
- Population Fidelity: Evaluating Population Representativeness in LLMs#1131095
- General Phrase Debiaser: Debiasing Masked Language Models at a Multi-Token Level#798545
- General Phrase Debiaser: Debiasing Masked Language Models at a Multi-Token Level#797132
- Black-Box Membership Inference via Word-Level Probability Estimation#997613
Record · ID 987381
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive