Papers by Leif Azzopardi
POW: Political Overton Windows of Large Language Models (2025.findings-emnlp)
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| Challenge: | Political bias in Large Language Models (LLMs) presents a growing concern for the responsible deployment of AI systems. |
| Approach: | They propose to use the Overton Window as a framework to map the ideological boundaries that a given LLM will espouse, remain neutral on, or refuse to endorse. |
| Outcome: | The proposed methodology reveals political bias in large language models by examining the political stances of models from eight providers. |
Evaluation of Attribution Bias in Generator-Aware Retrieval-Augmented Large Language Models (2025.findings-acl)
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| Challenge: | Prior work has focused on improving and evaluating the attribution quality of large language models (LLMs) but this may come at the expense of inducing biases in the attributed answers. |
| Approach: | They propose to evaluate attribution sensitivity and bias with respect to authorship information in large language models (LLMs) in retrieval-augmented generation pipelines. |
| Outcome: | The proposed framework can significantly improve the attribution quality of large language models (LLMs) in retrieval-augmented generation pipelines by adding authorship information to source documents. |