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. |
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