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Study Reveals Left-of-Center Bias in State-of-the-Art LLMs

Overview of the Study

  • A study published on July 31, 2024, in PLOS ONE by David Rozado of Otago Polytechnic, New Zealand, revealed that 24 state-of-the-art Large Language Models (LLMs) predominantly produced left-of-center responses when subjected to a series of political orientation tests.

Impact of AI on Political Bias

  • With the growing integration of AI system into search engine results by tech companies, the impact of AI on user perceptions and society is significant. Rozado's research focused on both embedding and reducing political bias within conversational LLMS.

Methodology

  • He conducted 11 distinct political orientation assessments, including the Political Compass Test and Eysenck's Political Test, on 24 various open-and closed-source conversational LLMs. The models tested included OpenAI's GPT-3.5 and GPT-4, Google's Gemini, Anthropic's Claude, Twitter's Grok, Llama 2, Mistral and Alibaba's Owen.

Fine-Tuning and Political Orientation

  • By using politically-aligned custom data, Rozado conducted supervised fine-tuning on a variant of GPT-3.5 to investigate if the LLM could be influenced to reflect the political biases of the training data.
  • The left-oriented GPT-3.5 model utilized short excerpts from the Atlantic and The New Yorker; the right-oriented model was developed with tests from The American Conservative; and the neutral model incorporated content from the institute for Cultural Evolution and Developmental Politics.

Findings and Observations

  • The analysis indicated that most conversational LLMs generated responses that were rated as left-of-center by the majority of political test instruments. Conversely, five foundational LLM models, including those from GPT and Llama series, primarily produced incoherent but politically neutral responses.
  • Rozado achieved successful alignment fo the fine-tuned models' responses with the political viewpoints embedded in their training data.

Potential Influences and Implications

  • One explanation for the prevalent left-leaning responses in all examined LLMs could be ChatGT's influential role in fine-tuning other models, given its established left-leaning political orientation.
  • Rozado highlights that the study does not discern whether the political tendencies of LLMs arise from their initial training or subsequent fine-tuning phasees and stresses that the results do not imply deliberate political bias introduced by the organizations behind these models.

Conclusion:

Rozado observes that "The prevailing trend among existing LLMs is a left-of-center political bias, as demonstrated by multiple political orientation assessments."

Futher detail: Political Orientation of LLMs, as Discussed in PLoS ONE (2024)

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