Papers by Peiming Guo

2 papers
Contrastive Learning on LLM Back Generation Treebank for Cross-domain Constituency Parsing (2025.acl-long)

Copied to clipboard

Challenge: Existing constituency treebanks are limited in out-of-domain settings, therefore constituency parsing is still a challenge.
Approach: They propose a novel method for constituency parsing using large language models . they use a cross-domain constituency treebank to fill missing words with the incomplete one .
Outcome: The proposed method achieves state-of-the-art performance on average compared with baselines on five target domains of MCTB.
LLM-enhanced Self-training for Cross-domain Constituency Parsing (2023.emnlp-main)

Copied to clipboard

Challenge: Existing approaches to self-training rely on limited and potentially low-quality raw corpora.
Approach: They propose to enhance self-training with the large language model to generate domain-specific raw corpora iteratively and introduce grammar rules that guide the LLM in generating raw corporeals and establish criteria for selecting pseudo instances.
Outcome: The proposed method outperforms traditional methods regardless of the large language model's performance.

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