Leveraging In-Context Learning for Political Bias Testing of LLMs (2025.acl-long)
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| Challenge: | Existing probing methods for evaluating LLMs with political questions have limited stability and are unreliable. |
| Approach: | They propose to use human survey data as in-context examples to query LLMs with political questions to evaluate their potential biases. |
| Outcome: | The proposed task improves the stability of question-based bias evaluation and may be used to compare instruction-tuned models to their base versions. |
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Biased LLMs can Influence Political Decision-Making (2025.acl-long)
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Jillian Fisher, Shangbin Feng, Robert Aron, Thomas Richardson, Yejin Choi, Daniel W Fisher, Jennifer Pan, Yulia Tsvetkov, Katharina Reinecke
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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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