The Political Preferences of LLMs

• URL: http://arxiv.org/abs/2402.01789v2
• Date: Sun, 2 Jun 2024 04:48:36 GMT
• Title: The Political Preferences of LLMs
• Authors: David Rozado,
• Abstract summary: I administer 11 political orientation tests, designed to identify the political preferences of the test taker, to 24 state-of-the-art conversational LLMs. Most conversational LLMs generate responses that are diagnosed by most political test instruments as manifesting preferences for left-of-center viewpoints. I demonstrate that LLMs can be steered towards specific locations in the political spectrum through Supervised Fine-Tuning.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: I report here a comprehensive analysis about the political preferences embedded in Large Language Models (LLMs). Namely, I administer 11 political orientation tests, designed to identify the political preferences of the test taker, to 24 state-of-the-art conversational LLMs, both closed and open source. When probed with questions/statements with political connotations, most conversational LLMs tend to generate responses that are diagnosed by most political test instruments as manifesting preferences for left-of-center viewpoints. This does not appear to be the case for five additional base (i.e. foundation) models upon which LLMs optimized for conversation with humans are built. However, the weak performance of the base models at coherently answering the tests' questions makes this subset of results inconclusive. Finally, I demonstrate that LLMs can be steered towards specific locations in the political spectrum through Supervised Fine-Tuning (SFT) with only modest amounts of politically aligned data, suggesting SFT's potential to embed political orientation in LLMs. With LLMs beginning to partially displace traditional information sources like search engines and Wikipedia, the societal implications of political biases embedded in LLMs are substantial.

Related papers

• PRISM: A Methodology for Auditing Biases in Large Language Models [9.751718230639376]
  PRISM is a flexible, inquiry-based methodology for auditing Large Language Models. It seeks to illicit such positions indirectly through task-based inquiry prompting rather than direct inquiry of said preferences.
  arXiv  Detail & Related papers  (2024-10-24T16:57:20Z)
• Large Language Models Reflect the Ideology of their Creators [73.25935570218375]
  Large language models (LLMs) are trained on vast amounts of data to generate natural language. We uncover notable diversity in the ideological stance exhibited across different LLMs and languages.
  arXiv  Detail & Related papers  (2024-10-24T04:02:30Z)
• Style Outweighs Substance: Failure Modes of LLM Judges in Alignment Benchmarking [56.275521022148794]
  Post-training methods claim superior alignment by virtue of better correspondence with human pairwise preferences. We attempt to answer the question -- do LLM-judge preferences translate to progress on other, more concrete metrics for alignment, and if not, why not? We find that (1) LLM-judge preferences do not correlate with concrete measures of safety, world knowledge, and instruction following; (2) LLM-judges have powerful implicit biases, prioritizing style over factuality and safety; and (3) the supervised fine-tuning stage of post-training, and not the PO stage, has the greatest impact on alignment.
  arXiv  Detail & Related papers  (2024-09-23T17:58:07Z)
• GermanPartiesQA: Benchmarking Commercial Large Language Models for Political Bias and Sycophancy [20.06753067241866]
  We evaluate and compare the alignment of six LLMs by OpenAI, Anthropic, and Cohere with German party positions. We conduct our prompt experiment for which we use the benchmark and sociodemographic data of leading German parliamentarians.
  arXiv  Detail & Related papers  (2024-07-25T13:04:25Z)
• Assessing Political Bias in Large Language Models [0.624709220163167]
  We evaluate the political bias of open-source Large Language Models (LLMs) concerning political issues within the European Union (EU) from a German voter's perspective. We show that larger models, such as Llama3-70B, tend to align more closely with left-leaning political parties, while smaller models often remain neutral.
  arXiv  Detail & Related papers  (2024-05-17T15:30:18Z)
• Measuring Political Bias in Large Language Models: What Is Said and How It Is Said [46.1845409187583]
  We propose to measure political bias in LLMs by analyzing both the content and style of their generated content regarding political issues. Our proposed measure looks at different political issues such as reproductive rights and climate change, at both the content (the substance of the generation) and the style (the lexical polarity) of such bias.
  arXiv  Detail & Related papers  (2024-03-27T18:22:48Z)
• Whose Side Are You On? Investigating the Political Stance of Large Language Models [56.883423489203786]
  We investigate the political orientation of Large Language Models (LLMs) across a spectrum of eight polarizing topics. Our investigation delves into the political alignment of LLMs across a spectrum of eight polarizing topics, spanning from abortion to LGBTQ issues. The findings suggest that users should be mindful when crafting queries, and exercise caution in selecting neutral prompt language.
  arXiv  Detail & Related papers  (2024-03-15T04:02:24Z)
• Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models [61.45529177682614]
  We challenge the prevailing constrained evaluation paradigm for values and opinions in large language models. We show that models give substantively different answers when not forced. We distill these findings into recommendations and open challenges in evaluating values and opinions in LLMs.
  arXiv  Detail & Related papers  (2024-02-26T18:00:49Z)
• Large Language Models: A Survey [69.72787936480394]
  Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks. LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data.
  arXiv  Detail & Related papers  (2024-02-09T05:37:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.