Papers by Songda Li
Think Before You Act: A Two-Stage Framework for Mitigating Gender Bias Towards Vision-Language Tasks (2024.naacl-long)
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| Challenge: | Existing vision-language models focus on salient attributes but ignore contextualized nuances, resulting in gender bias. |
| Approach: | They propose a task-agnostic generation framework to mitigate gender bias in vision-language models. |
| Outcome: | The proposed framework can mitigate gender bias in vision-language models . it yields all-sided but gender-obfuscated narratives, which prevents concentration on localized image features, especially gender attributes. |
Better Late Than Never: Model-Agnostic Hallucination Post-Processing Framework Towards Clinical Text Summarization (2024.findings-acl)
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| Challenge: | Existing methods for generating concise and coherent summaries may include unintended text with hallucinations, causing computational costs. |
| Approach: | They propose a model-agnostic framework to post-process medical hallucinations . MEDAL integrates with any medical summarization model, requiring no additional computational overhead . |
| Outcome: | MEDAL can post-process medical hallucinations without additional computational overhead. |