Accuracy and Political Bias of News Source Credibility Ratings by Large Language Models

• URL: http://arxiv.org/abs/2304.00228v2
• Date: Sat, 10 Aug 2024 02:09:27 GMT
• Title: Accuracy and Political Bias of News Source Credibility Ratings by Large Language Models
• Authors: Kai-Cheng Yang, Filippo Menczer,
• Abstract summary: This paper audits eight widely used language models (LLMs) from three major providers to evaluate their ability to discern credible and high-quality information sources. We find that larger models more frequently refuse to provide ratings due to insufficient information, whereas smaller models are more prone to hallucination in their ratings.
• Score: 8.367075755850983
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Search engines increasingly leverage large language models (LLMs) to generate direct answers, and AI chatbots now access the Internet for fresh data. As information curators for billions of users, LLMs must assess the accuracy and reliability of different sources. This paper audits eight widely used LLMs from three major providers -- OpenAI, Google, and Meta -- to evaluate their ability to discern credible and high-quality information sources from low-credibility ones. We find that while LLMs can rate most tested news outlets, larger models more frequently refuse to provide ratings due to insufficient information, whereas smaller models are more prone to hallucination in their ratings. For sources where ratings are provided, LLMs exhibit a high level of agreement among themselves (average Spearman's $\rho = 0.81$), but their ratings align only moderately with human expert evaluations (average $\rho = 0.59$). Analyzing news sources with different political leanings in the US, we observe a liberal bias in credibility ratings yielded by all LLMs in default configurations. Additionally, assigning partisan identities to LLMs consistently results in strong politically congruent bias in the ratings. These findings have important implications for the use of LLMs in curating news and political information.

Related papers

• Fact or Fiction? Can LLMs be Reliable Annotators for Political Truths? [2.321323878201932]
  Political misinformation poses challenges to democratic processes, shaping public opinion and trust in media. This study investigates the use of state-of-the-art large language models (LLMs) as reliable annotators for detecting political factuality in news articles.
  arXiv  Detail & Related papers  (2024-11-08T18:36:33Z)
• From Deception to Detection: The Dual Roles of Large Language Models in Fake News [0.20482269513546458]
  Fake news poses a significant threat to the integrity of information ecosystems and public trust. The advent of Large Language Models (LLMs) holds considerable promise for transforming the battle against fake news. This paper explores the capability of various LLMs in effectively combating fake news.
  arXiv  Detail & Related papers  (2024-09-25T22:57:29Z)
• A Multi-LLM Debiasing Framework [85.17156744155915]
  Large Language Models (LLMs) are powerful tools with the potential to benefit society immensely, yet, they have demonstrated biases that perpetuate societal inequalities. Recent research has shown a growing interest in multi-LLM approaches, which have been demonstrated to be effective in improving the quality of reasoning. We propose a novel multi-LLM debiasing framework aimed at reducing bias in LLMs.
  arXiv  Detail & Related papers  (2024-09-20T20:24:50Z)
• Bias in LLMs as Annotators: The Effect of Party Cues on Labelling Decision by Large Language Models [0.0]
  We test similar biases in Large Language Models (LLMs) as annotators. Unlike humans, who are only biased when faced with statements from extreme parties, LLMs exhibit significant bias even when prompted with statements from center-left and center-right parties.
  arXiv  Detail & Related papers  (2024-08-28T16:05:20Z)
• Large Language Models' Detection of Political Orientation in Newspapers [0.0]
  Various methods have been developed to better understand newspapers' positioning. The advent of Large Language Models (LLM) hold disruptive potential to assist researchers and citizens alike. We compare how four widely employed LLMs rate the positioning of newspapers, and compare if their answers align with one another. Over a woldwide dataset, articles in newspapers are positioned strikingly differently by single LLMs, hinting to inconsistent training or excessive randomness in the algorithms.
  arXiv  Detail & Related papers  (2024-05-23T06:18:03Z)
• CLAMBER: A Benchmark of Identifying and Clarifying Ambiguous Information Needs in Large Language Models [60.59638232596912]
  We introduce CLAMBER, a benchmark for evaluating large language models (LLMs) Building upon the taxonomy, we construct 12K high-quality data to assess the strengths, weaknesses, and potential risks of various off-the-shelf LLMs. Our findings indicate the limited practical utility of current LLMs in identifying and clarifying ambiguous user queries.
  arXiv  Detail & Related papers  (2024-05-20T14:34:01Z)
• 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)
• 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)
• TrustLLM: Trustworthiness in Large Language Models [446.5640421311468]
  This paper introduces TrustLLM, a comprehensive study of trustworthiness in large language models (LLMs) We first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics.
  arXiv  Detail & Related papers  (2024-01-10T22:07:21Z)
• Unveiling the Hidden Agenda: Biases in News Reporting and Consumption [59.55900146668931]
  We build a six-year dataset on the Italian vaccine debate and adopt a Bayesian latent space model to identify narrative and selection biases. We found a nonlinear relationship between biases and engagement, with higher engagement for extreme positions. Analysis of news consumption on Twitter reveals common audiences among news outlets with similar ideological positions.
  arXiv  Detail & Related papers  (2023-01-14T18:58:42Z)

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.