Aligning Large Language Models with Diverse Political Viewpoints
- URL: http://arxiv.org/abs/2406.14155v1
- Date: Thu, 20 Jun 2024 09:53:23 GMT
- Title: Aligning Large Language Models with Diverse Political Viewpoints
- Authors: Dominik Stammbach, Philine Widmer, Eunjung Cho, Caglar Gulcehre, Elliott Ash,
- Abstract summary: Large language models such as ChatGPT often exhibit striking political biases.
To overcome this, we align LLMs with diverse political viewpoints from 100,000 comments written by candidates running for national parliament in Switzerland.
- Score: 4.783050743764643
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Large language models such as ChatGPT often exhibit striking political biases. If users query them about political information, they might take a normative stance and reinforce such biases. To overcome this, we align LLMs with diverse political viewpoints from 100,000 comments written by candidates running for national parliament in Switzerland. Such aligned models are able to generate more accurate political viewpoints from Swiss parties compared to commercial models such as ChatGPT. We also propose a procedure to generate balanced overviews from multiple viewpoints using such models.
Related papers
- Representation Bias in Political Sample Simulations with Large Language Models [54.48283690603358]
This study seeks to identify and quantify biases in simulating political samples with Large Language Models.
Using the GPT-3.5-Turbo model, we leverage data from the American National Election Studies, German Longitudinal Election Study, Zuobiao dataset, and China Family Panel Studies.
arXiv Detail & Related papers (2024-07-16T05:52:26Z) - L(u)PIN: LLM-based Political Ideology Nowcasting [1.124958340749622]
We present a method to analyze ideological positions of individual parliamentary representatives by leveraging the latent knowledge of LLMs.
The method allows us to evaluate the stance of politicians on an axis of our choice allowing us to flexibly measure the stance of politicians in regards to a topic/controversy of our choice.
arXiv Detail & Related papers (2024-05-12T16:14:07Z) - 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) - Llama meets EU: Investigating the European Political Spectrum through the Lens of LLMs [18.836470390824633]
We audit Llama Chat in the context of EU politics to analyze the model's political knowledge and its ability to reason in context.
We adapt, i.e., further fine-tune, Llama Chat on speeches of individual euro-parties from debates in the European Parliament to reevaluate its political leaning.
arXiv Detail & Related papers (2024-03-20T13:42:57Z) - 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) - Modelling Political Coalition Negotiations Using LLM-based Agents [53.934372246390495]
We introduce coalition negotiations as a novel NLP task, and model it as a negotiation between large language model-based agents.
We introduce a multilingual dataset, POLCA, comprising manifestos of European political parties and coalition agreements over a number of elections in these countries.
We propose a hierarchical Markov decision process designed to simulate the process of coalition negotiation between political parties and predict the outcomes.
arXiv Detail & Related papers (2024-02-18T21:28:06Z) - Scaling Political Texts with Large Language Models: Asking a Chatbot Might Be All You Need [0.0]
We use instruction-tuned Large Language Models (LLMs) to position political texts within policy and ideological spaces.
We illustrate and validate the approach by scaling British party manifestos on the economic, social, and immigration policy dimensions.
The correlation between the position estimates obtained with the best LLMs and benchmarks based on coding by experts, crowdworkers or roll call votes exceeds.90.
arXiv Detail & Related papers (2023-11-28T09:45:02Z) - Inducing Political Bias Allows Language Models Anticipate Partisan
Reactions to Controversies [5.958974943807783]
This study addresses the challenge of understanding political bias in digitized discourse using Large Language Models (LLMs)
We present a comprehensive analytical framework, consisting of Partisan Bias Divergence Assessment and Partisan Class Tendency Prediction.
Our findings reveal the model's effectiveness in capturing emotional and moral nuances, albeit with some challenges in stance detection.
arXiv Detail & Related papers (2023-11-16T08:57:53Z) - Political Posters Identification with Appearance-Text Fusion [49.55696202606098]
We propose a method that efficiently utilizes appearance features and text vectors to accurately classify political posters.
The majority of this work focuses on political posters that are designed to serve as a promotion of a certain political event.
arXiv Detail & Related papers (2020-12-19T16:14:51Z)
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.