This study challenges the prevailing approach of measuring political leanings in Large Language Models (LLMs) through direct questioning. By extensively testing LLMs with original, positively and negatively paraphrased Political Compass questions we demonstrate that LLMs do not consistently reveal their political biases in response to standard questions. Our findings indicate that LLMs' political orientations are elusive, easily influenced by subtle changes in phrasing and context. This study underscores the limitations of direct questioning in accurately measuring the political biases of LLMs and emphasizes the necessity for more refined and effective approaches to understand their true political stances.
The Elusiveness of Detecting Political Bias in Language Models
Riccardo Lunardi
Primo
;David La Barbera
Secondo
;Kevin Roitero
Ultimo
2024-01-01
Abstract
This study challenges the prevailing approach of measuring political leanings in Large Language Models (LLMs) through direct questioning. By extensively testing LLMs with original, positively and negatively paraphrased Political Compass questions we demonstrate that LLMs do not consistently reveal their political biases in response to standard questions. Our findings indicate that LLMs' political orientations are elusive, easily influenced by subtle changes in phrasing and context. This study underscores the limitations of direct questioning in accurately measuring the political biases of LLMs and emphasizes the necessity for more refined and effective approaches to understand their true political stances.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
