# Left, Right, or Center? Evaluating LLM Framing in News Classification and Generation

arXiv: https://arxiv.org/abs/2601.05835 · HTML: https://mlsanity.com/paper/2601.05835/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 23 references that are in the map.
- First posted on arXiv 9 Jan 2026.

## Builds on (most cited first, 17 of 17)

- [LLMs instead of Human Judges? A Large Scale Empirical Study across 20 NLP Evaluation Tasks](https://mlsanity.com/paper/2406.18403/) (2024)
- [We Can Detect Your Bias: Predicting the Political Ideology of News Articles](https://mlsanity.com/paper/2010.05338/) (2020)
- [Measuring Political Bias in Large Language Models: What Is Said and How It Is Said](https://mlsanity.com/paper/2403.18932/) (2024)
- [In Plain Sight: Media Bias Through the Lens of Factual Reporting](https://mlsanity.com/paper/1909.02670/) (2019)
- [Moral Framing and Ideological Bias of News](https://mlsanity.com/paper/2009.12979/) (2020)
- [Multi-view Models for Political Ideology Detection of News Articles](https://mlsanity.com/paper/1809.03485/) (2018)
- [Evaluating the Prompt Steerability of Large Language Models](https://mlsanity.com/paper/2411.12405/) (2024)
- [Understanding and Mitigating Overrefusal in LLMs from an Unveiling Perspective of Safety Decision Boundary](https://mlsanity.com/paper/2505.18325/) (2025)
- [The Media Bias Taxonomy: A Systematic Literature Review on the Forms and Automated Detection of Media Bias](https://mlsanity.com/paper/2312.16148/) (2023)
- [Is Safer Better? The Impact of Guardrails on the Argumentative Strength of LLMs in Hate Speech Countering](https://mlsanity.com/paper/2410.03466/) (2024)
- [Quantifying Generative Media Bias with a Corpus of Real-world and Generated News Articles](https://mlsanity.com/paper/2406.10773/) (2024)
- [Analyzing Political Bias in LLMs via Target-Oriented Sentiment Classification](https://mlsanity.com/paper/2505.19776/) (2025)
- [Decoding News Narratives: A Critical Analysis of Large Language Models in Framing Detection](https://mlsanity.com/paper/2402.11621/) (2024)
- [Media Bias Detector: Designing and Implementing a Tool for Real-Time Selection and Framing Bias Analysis in News Coverage](https://mlsanity.com/paper/2502.06009/) (2025)
- [P^3SUM: Preserving Author's Perspective in News Summarization with Diffusion Language Models](https://mlsanity.com/paper/2311.09741/) (2023)
- [Safety and Security Analysis of Large Language Models: Benchmarking Risk Profile and Harm Potential](https://mlsanity.com/paper/2509.10655/) (2025)
- [Leveraging In-Context Learning for Political Bias Testing of LLMs](https://mlsanity.com/paper/2506.22232/) (2025)

## Built on by (most cited first, 1 of 1)

- [Grokipedia vs Wikipedia: An LLM-Based Audit of Political Neutrality along Ideologies](https://mlsanity.com/paper/2607.15146/) (2026)

## Abstract (arXiv)

Large Language Model (LLM) based summarization and text generation are increasingly used for producing and rewriting text, raising concerns about political framing in journalism where subtle wording choices can shape interpretation. Across nine state-of-the-art LLMs, we study political framing by testing whether LLMs' classification-based bias signals align with framing behavior in their generated summaries. We first compare few-shot ideology predictions against LEFT/CENTER/RIGHT labels. We then generate "steered" summaries under FAITHFUL, CENTRIST, LEFT, and RIGHT prompts, and score all outputs using a single fixed ideology evaluator. We find pervasive ideological center-collapse in both article-level ratings and generated text, indicating a systematic tendency toward centrist framing. Among evaluated models, Grok 4 is by far the most ideologically expressive generator, while Claude Sonnet 4.5 and Llama 3.1 achieve the strongest bias-rating performance among commercial and open-weight models, respectively.