Papers by Xinyun Liu

2 papers
Exploring Label Hierarchy in a Generative Way for Hierarchical Text Classification (2022.coling-1)

Copied to clipboard

Challenge: Existing methods for hierarchical text classification are lacking in the field of natural language processing.
Approach: They propose a hierarchy-aware T5 model with path-adaptive attention mechanism to exploit hierarchical dependency across different levels.
Outcome: The proposed model outperforms state-of-the-art models especially in Macro-F1 and low Macro.
Generalizable LLM Learning of Graph Synthetic Data with Post-training Alignment (2026.findings-acl)

Copied to clipboard

Challenge: Existing research has focused on enhancing graph reasoning capabilities of LLMs by supervised fine-tuning on synthetic graph data.
Approach: They propose to unlock generalizable learning of graph with post-training alignment with synthetic graph data by aligning off-the-shelf LLMs and LLM fine-tuned on synthetic graphs.
Outcome: The proposed algorithm improves on synthetic graph problems and out-of-domain tasks with implicit graph structures.

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