Papers by Helan Hu

2 papers
Rethinking Semantic Parsing for Large Language Models: Enhancing LLM Performance with Semantic Hints (2025.acl-short)

Copied to clipboard

Challenge: Semantic Parsing improves performance of smaller models, but it is unclear whether it extends similarly to large language models.
Approach: They propose a prompting approach that embeds semantic hints within the prompt to improve LLM performance.
Outcome: The proposed approach improves LLMs’ performance across various tasks, highlighting the potential of integrating semantic information to improve LLM capabilities.
Improving the Robustness of Distantly-Supervised Named Entity Recognition via Uncertainty-Aware Teacher Learning and Student-Student Collaborative Learning (2024.findings-acl)

Copied to clipboard

Challenge: Named Entity Recognition (NER) methods require a substantial quantity of high-quality annotation for training models.
Approach: They propose a method to reduce the number of incorrect pseudo labels in self-training . they propose 'uncertainty-aware teacher learning' and 'student-student collaboration'
Outcome: The proposed method is superior to state-of-the-art DS-NER denoising methods.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations