Geopolitical Parallax: Beyond Walter Lippmann Just After Large Language Models
Mehmet Can Yavuz, Humza Gohar Kabir, Aylin \"Ozkan

TL;DR
This paper examines how large language models from Chinese and Western origins differ in news content evaluation, revealing persistent geopolitical biases that impact perceived news quality and subjectivity, emphasizing the need for cultural calibration in LLM applications.
Contribution
It introduces a systematic comparison of Chinese and Western LLMs in news quality assessment, highlighting model-origin biases and their implications for media evaluation.
Findings
Western models score higher on subjectivity and positive emotion in Palestine coverage.
Chinese models emphasize novelty and descriptiveness, with lower fluency and technicality scores.
Bias patterns align with media bias theory and differ across geopolitical topics.
Abstract
Objectivity in journalism has long been contested, oscillating between ideals of neutral, fact-based reporting and the inevitability of subjective framing. With the advent of large language models (LLMs), these tensions are now mediated by algorithmic systems whose training data and design choices may themselves embed cultural or ideological biases. This study investigates geopolitical parallax-systematic divergence in news quality and subjectivity assessments-by comparing article-level embeddings from Chinese-origin (Qwen, BGE, Jina) and Western-origin (Snowflake, Granite) model families. We evaluate both on a human-annotated news quality benchmark spanning fifteen stylistic, informational, and affective dimensions, and on parallel corpora covering politically sensitive topics, including Palestine and reciprocal China-United States coverage. Using logistic regression probes and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
