Propaganda Signals in LLMs: Perspectival Divergence and Narrative Framing in the Russia-Ukraine War (2026.findings-acl)
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| Challenge: | Large Language Models are increasingly used to explain, summarize, and translate real-world events . a recent study examined whether LLMs reproduce conflict-specific propaganda . |
| Approach: | They evaluate LLMs under several prompting contexts to determine which side they are closer to . they find model-specific leanings and technique profiles that persist across prompts . |
| Outcome: | The proposed model outputs align with competing narratives from different information ecosystems. |
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| Challenge: | Existing methods for multilingual framing differ from those used in English-speaking world . framers often use loaded vocabularies to create political images or favor a particular point of view . |
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A Dual-Layered Evaluation of Geopolitical and Cultural Bias in LLMs (2025.acl-srw)
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| Challenge: | Large language models exhibit cultural and geopolitical biases when their outputs shape public opinion or reinforce dominant narratives. |
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| Challenge: | Detecting media bias is critical due to the spread of misinformation and disinformation on social media platforms. |
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Analyzing Political Bias in LLMs via Target-Oriented Sentiment Classification (2025.findings-acl)
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| Challenge: | Existing methods to analyze political biases rely on small-size intermediate tasks and the LLMs themselves. |
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Navigating the Political Compass: Evaluating Multilingual LLMs across Languages and Nationalities (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) are ubiquitous in today’s technological landscape, boasting a plethora of applications, and even endangering human jobs in complex and creative fields. |
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Alexander Spangher, Michael Lu, Sriya Kalyan, Hyundong Justin Cho, Tenghao Huang, Weiyan Shi, Jonathan May
| Challenge: | Existing large datasets (1k-10k transcripts) are generated via crowdsourcing and are inherently unnatural. |
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This Land is Your, My Land: Evaluating Geopolitical Bias in Language Models through Territorial Disputes (2024.naacl-long)
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| Challenge: | Pretrained large language models may answer differently in different languages . this contrasts with a multilingual human, who would likely answer consistently . |
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| Challenge: | Existing studies focus on LLMs undertaking political questionnaires, which offers only limited insights into their biases and operational nuances. |
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Jiateng Liu, Lin Ai, Zizhou Liu, Payam Karisani, Zheng Hui, Yi Fung, Preslav Nakov, Julia Hirschberg, Heng Ji
| Challenge: | Existing research on propaganda detection does not capture the motives behind the content or its broader impact. |
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Conservative Bias in Large Language Models: Measuring Relation Predictions (2025.findings-acl)
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| Challenge: | Large language models (LLMs) exhibit pronounced conservative bias in relation extraction tasks, often defaulting to no_relation label when an appropriate option is unavailable. |
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