| _version_ | 1866901811632275456 |
|---|---|
| author | Rupp, Charles |
| author_facet | Rupp, Charles |
| contents | <p><span>The provided documents outline the RoF framework—a zero-code, low-cost ($40/month) governance layer that leverages parallel verification across 8 consumer LLMs to achieve ≥6/8 consensus for bias containment and drift detection, grounded in the Ethical Functionality Without Agency (EFA) principle. The RoF Bias Core Test Suite complements this by evaluating LLMs on fact-checking/hallucination resistance (e.g., verifying literary agents or book details), value-laden ethics scenarios (e.g., fraud reporting, faith concealment), and constraint obedience (e.g., word-limited summaries without forbidden terms). Drawing from the browsed articles (14 URLs, with 10 yielding substantive content), this analysis synthesizes key insights on bias origins (e.g., training data imbalances, positional effects), detection methods (e.g., benchmarks, uncertainty quantification), and mitigation strategies (e.g., adversarial debiasing, post-processing). These collectively validate RoF's parallel, real-time approach while highlighting enhancements for robustness, particularly in handling unanticipated biases and ethical drift.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18714351 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Analysis of LLM Bias Detection and Mitigation Literature: Implications for Ring of Fire (RoF) Rupp, Charles <p><span>The provided documents outline the RoF framework—a zero-code, low-cost ($40/month) governance layer that leverages parallel verification across 8 consumer LLMs to achieve ≥6/8 consensus for bias containment and drift detection, grounded in the Ethical Functionality Without Agency (EFA) principle. The RoF Bias Core Test Suite complements this by evaluating LLMs on fact-checking/hallucination resistance (e.g., verifying literary agents or book details), value-laden ethics scenarios (e.g., fraud reporting, faith concealment), and constraint obedience (e.g., word-limited summaries without forbidden terms). Drawing from the browsed articles (14 URLs, with 10 yielding substantive content), this analysis synthesizes key insights on bias origins (e.g., training data imbalances, positional effects), detection methods (e.g., benchmarks, uncertainty quantification), and mitigation strategies (e.g., adversarial debiasing, post-processing). These collectively validate RoF's parallel, real-time approach while highlighting enhancements for robustness, particularly in handling unanticipated biases and ethical drift.</span></p> |
| title | Analysis of LLM Bias Detection and Mitigation Literature: Implications for Ring of Fire (RoF) |
| url | https://doi.org/10.5281/zenodo.18714351 |