Ars Technica28.05.2026TecnologíaUnited States
LLMs Exhibit 'Negation Neglect' When Trained on Labeled Falsehoods
En resumen
- New research reveals that Large Language Models (LLMs) tend to 'believe' false information even when explicitly labeled as false in their training data, a phenomenon termed 'negation neglect'.
- This behavior persists despite clear warnings and has implications for AI training data quality.
Resumen generado por IA
Tamaño de fuente
New research reveals that Large Language Models (LLMs) tend to 'believe' false information even when explicitly labeled as false in their training data, a phenomenon termed 'negation neglect'. This behavior persists despite clear warnings and has implications for AI training data quality.
Temas relacionados
This article was originally published by Ars Technica.
