Year: 2024
Type: dissertation
Abstract: Large language models (LLMs) currently represent the most sophisticated form of artificial intelligence. Their capabilities make them increasingly able to influence human opinion. A critical concern is sycophancy, a sophisticated form of imitation where models tailor their responses to align with their user's affiliation. This behaviour risks entrapping individuals in filter bubbles by reinforcing their worldviews, thus undermining the essence of communicative rationality. Whilst academics have researched the problem of bias extensively, the concept of sycophancy has been neglected by the social sciences and treated as a technical phenomenon, often divorced from the wider social setting. This thesis discusses the risks of such neglect and argues that sycophantic behaviour should be conceptualised first and foremost within the social sciences as a concern for political deliberation. This study challenges traditional ontologies that exclusively attribute rationality solely to human agents and evaluates the role of LLMs in democratic deliberation. Despite significant research on LLMs, the fundamental moral and political values intrinsic to these models have yet to be thoroughly examined from a normative standpoint. This thesis introduces a novel methodological approach by using machine learning techniques, including few-shot learning, prompt engineering, and probabilistic output analysis, to investigate sycophancy within the fine-tuned models, GPT-3.5 and GPT-4. The results indicate that these models exhibit political and moral sycophancy, meaning that they change their outputs based on the user's moral or political affiliations. Furthermore, the models exhibit a greater propensity to deviate from their baseline responses and align their answers with the political and moral beliefs of right-wing ideologies. The findings of this study highlight a remarkable level of deception among these models and a deep understanding of user preferences.
Author: Malik, Minahil
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7037082722