Papers by Yiyun Zhao
How does BERT’s attention change when you fine-tune? An analysis methodology and a case study in negation scope (2020.acl-main)
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| Challenge: | Recent work probing pre-trained language models for downstream tasks is difficult to explain . a growing body of research is devoted to understanding what linguistic properties these language models have acquired. |
| Approach: | They propose a procedure and analysis method that takes a hypothesis of how a transformer-based model might encode a linguistic phenomenon and tests its validity. |
| Outcome: | The proposed method tests a hypothesis that some attention heads will consistently attend from a word in negation scope to the negation cue. |
Systematic Evaluation of Long-Context LLMs on Financial Concepts (2024.emnlp-industry)
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| Challenge: | Long-context large language models (LC LLMs) are promising for tasks with long context windows . however, their ability to reliably utilize their growing context windows remains under investigation . |
| Approach: | They evaluate the performance of long-context large language models using a real-world financial news dataset. |
| Outcome: | The proposed models exhibit brittleness at longer context lengths even for simple tasks, the authors show . they advocate for more rigorous evaluation of LC LLMs by employing holistic metrics such as F1 (rather than recall) |
Importance of Synthesizing High-quality Data for Text-to-SQL Parsing (2023.findings-acl)
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Yiqun Hu, Yiyun Zhao, Jiarong Jiang, Wuwei Lan, Henghui Zhu, Anuj Chauhan, Alexander Hanbo Li, Lin Pan, Jun Wang, Chung-Wei Hang, Sheng Zhang, Jiang Guo, Mingwen Dong, Joseph Lilien, Patrick Ng, Zhiguo Wang, Vittorio Castelli, Bing Xiang
| Challenge: | Existing text-to-SQL parsers lack the data to perform well with augmented synthetic data. |
| Approach: | They propose a framework that imposes strong typing constraints and incorporates key relationships from schema. |
| Outcome: | The proposed framework improves on the high-quality synthesized SQL and natural language question (NLQ) models have significant accuracy boosts and achieve new state-of-the-art performance on spider. |
A Comparison of Strategies for Source-Free Domain Adaptation (2022.acl-long)
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| Challenge: | Existing research on domain adaptation without access to training data is limited due to privacy concerns. |
| Approach: | They compare active learning, self-training, and data augmentation strategies for source-free domain adaptation with a shared task. |
| Outcome: | The proposed algorithms yield consistent gains across all SemEval 2021 Task 10 tasks and domains, but they are unreliable for source-free domain adaptation. |
When and how to paraphrase for named entity recognition? (2023.acl-long)
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Saket Sharma, Aviral Joshi, Yiyun Zhao, Namrata Mukhija, Hanoz Bhathena, Prateek Singh, Sashank Santhanam
| Challenge: | Named entity recognition (NER) is a key component underpinning many industrial pipelines for a variety of downstream applications. |
| Approach: | They propose to use back translation to annotate entity spans in generations and propose a paraphraser with a larger dataset. |
| Outcome: | The proposed method improves NER performance across different datasets with gold annotations and paraphrasing strength. |