Papers by Xiaozhe Yang
NER-guided Comprehensive Hierarchy-aware Prompt Tuning for Hierarchical Text Classification (2024.lrec-main)
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| Challenge: | Hierarchical text classification (HTC) is a challenging task in natural language processing due to its complex taxonomic label hierarchy. |
| Approach: | They propose to use prompts to model hierarchical text classification (HTC) they propose to introduce conditional random fields and Global Pointer to establish hierarchic dependencies . |
| Outcome: | The proposed approach achieves state-of-the-art (SoTA) performance on three public datasets. |
Forge: Quality-Aware Reinforcement Learning for NP-Hard Optimization in LLMs (2026.findings-acl)
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| Challenge: | Existing benchmarks focus on correctness, overlooking optimality . large language models excel at math, coding, logic and puzzles . |
| Approach: | They propose a framework for training and evaluating Large Language Models on NP-hard optimization problems through quality-aware RLVR. |
| Outcome: | The proposed framework outperforms existing benchmarks on math, coding, logic and puzzles. |
CAME: Confidence-guided Adaptive Memory Efficient Optimization (2023.acl-long)
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| Challenge: | Existing memory-efficient methods require second-moment estimates of the per-parameter gradients to maintain their performance. |
| Approach: | They propose to use memory-efficient optimizers to reduce memory usage by preserving second-moment estimates of gradients. |
| Outcome: | The proposed method achieves fast convergence and lower memory usage across training tasks. |