Papers by Nannan Huang
When Bigger Isn’t Better: A Comprehensive Fairness Evaluation of Political Bias in Multi-News Summarisation (2026.acl-long)
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| Challenge: | Existing models that deal with multiple sources can exhibit political biases, causing unequal representation of viewpoints and underrepresentation of minority voices. |
| Approach: | They examine how large language models handle sources with varying political leanings using a dataset with political orientation labels. |
| Outcome: | The proposed model outperforms larger models and offers the best balance of fairness and efficiency. |
Less Is More? Examining Fairness in Pruned Large Language Models for Summarising Opinions (2025.emnlp-main)
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| Challenge: | reducing the size of LLMs through post-training pruning has been studied, but its impact on model fairness remains unexplored. |
| Approach: | They propose a pruning method that removes parameters that are redundant for input processing but influential in output generation. |
| Outcome: | The proposed pruning method can maintain or improve fairness across models and tasks where existing methods have limitations. |
Bias in Opinion Summarisation from Pre-training to Adaptation: A Case Study in Political Bias (2024.eacl-long)
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| Challenge: | Existing studies have focused on extractive summarisation but limited attention has been paid to abstractive summaries. |
| Approach: | They propose to trace bias in abstractive summarisation models to social media opinions using different models and adaptation methods. |
| Outcome: | The proposed model is compared with other models and adaptation methods to summarise social media opinions using different models and adaption methods. |