Papers by Siyu Miao
Word Matters: What Influences Domain Adaptation in Summarization? (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) can generalize domain datasets unseen during training but are not able to predict domain adaptation performance. |
| Approach: | They propose to quantify dataset learning difficulty as the learning difficulty of generative summarization, which is determined by word-based compression rate and abstraction level. |
| Outcome: | The proposed model can predict performance on unknown domain datasets without training, and it is based on the findings. |
Multilingual Retrieval Augmented Generation for Culturally-Sensitive Tasks: A Benchmark for Cross-lingual Robustness (2025.findings-acl)
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Bryan Li, Fiona Luo, Samar Haider, Adwait Agashe, Siyu Li, Runqi Liu, Miranda Muqing Miao, Shriya Ramakrishnan, Yuan Yuan, Chris Callison-Burch
| Challenge: | Existing approaches to retrieval-augmented generated (RAG) can be useful in multilingual settings, but they also introduce biases in the retrieved documents. |
| Approach: | They propose a dataset of territorial disputes paired with retrieved Wikipedia documents in 49 languages to evaluate cross-lingual robustness. |
| Outcome: | The proposed paradigm helps mitigate hallucinations of large language models (LLMs). |