Papers by Gabrielle Kaili-May Liu
MetaFaith: Faithful Natural Language Uncertainty Expression in LLMs (2025.emnlp-main)
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| Challenge: | Existing methods for faithful calibration of large language models (LLMs) are insufficient and can harm faithful calibration. |
| Approach: | They propose a new prompt-based calibration approach inspired by human metacognition that measures faithfulness across diverse models and task domains and enables up to 61% improvement in faithfulness. |
| Outcome: | The proposed approach improves faithfulness across diverse models and task domains and achieves an 83% win rate over original generations as judged by humans. |
MDCure: A Scalable Pipeline for Multi-Document Instruction-Following (2025.acl-long)
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| Challenge: | Multi-document (MD) processing is crucial for LLMs to handle real-world tasks such as summarization and question-answering across large sets of documents. |
| Approach: | They propose a framework that generates high-quality synthetic MD instruction data over sets of articles via targeted prompts. |
| Outcome: | MDCure generates high-quality synthetic MD instruction data over sets of articles . evaluations show it improves over pre-trained models by up to 75.1% . |