Papers by Longhui Zhang
Chinese Sequence Labeling with Semi-Supervised Boundary-Aware Language Model Pre-training (2024.lrec-main)
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| Challenge: | Pretrained language models (PLMs) have been successful in addressing word boundaries in Chinese sequence labeling tasks, but they rarely consider boundary information explicitly. |
| Approach: | They propose a method to integrate unsupervised boundary information into Chinese BERT's pre-training objectives and a supervised boundary-aware PLM. |
| Outcome: | The proposed model outperforms the vanilla version on Chinese sequence labeling tasks and in broader Chinese natural language understanding tasks. |
A Two-Stage Adaptation of Large Language Models for Text Ranking (2024.findings-acl)
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| Challenge: | Recent advances in pre-trained language models (PLMs) have significantly improved ranking performance in text ranking tasks. |
| Approach: | They propose a two-stage progressive paradigm to better adapt LLMs to text ranking by conducting continual pre-training on a large weakly-supervised corpus and performing SFT on high-quality data. |
| Outcome: | The proposed approach outperforms previous methods on in- and out-domain scenarios. |
A Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling (2021.emnlp-main)
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| Challenge: | Table filling based relational triple extraction methods focus on using local features but ignore the global associations of relations and token pairs, which increases the possibility of overlooking some important information during triple extraction. |
| Approach: | They propose a global feature-oriented triple extraction model that makes full use of the two kinds of global associations of relations and token pairs. |
| Outcome: | The proposed model achieves state-of-the-art on three benchmark datasets. |
A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation (2021.emnlp-main)
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| Challenge: | Existing knowledge-grounded dialogues perform poorly when transfer into new domains with limited training samples. |
| Approach: | They propose a weakly supervised three-stage learning framework based on weakly-supervised learning based upon large scale ungrounded dialogues and unstructured knowledge base. |
| Outcome: | The proposed framework outperforms state-of-the-art methods even in zero-resource setting. |
Speed Up Your Code: Progressive Code Acceleration Through Bidirectional Tree Editing (2025.acl-long)
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Longhui Zhang, Jiahao Wang, Meishan Zhang, GaoXiong Cao, Ensheng Shi, Mayuchi Mayuchi, Jun Yu, Honghai Liu, Jing Li, Min Zhang
| Challenge: | Existing training methods, such as direct instruction fine-tuning, overlook hierarchical relationships among acceleration patterns. |
| Approach: | They propose a new training paradigm that uses bidirectional tree editing and progressive code acceleration learning to improve LLMs’ CA capabilities. |
| Outcome: | The proposed training paradigm outperforms prompt-enhanced GPT-4 and current training-based methods on average across five programming languages. |
Forward-Backward Reasoning in Large Language Models for Mathematical Verification (2024.findings-acl)
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| Challenge: | Extensive experiments on six standard mathematical data sets and three LLMs show that FOBAR achieves state-of-the-art performance. |
| Approach: | They propose to combine forward and backward reasoning to verify candidate answers . they propose to use a template to mask a number and ask the LLM to answer a backward question . |
| Outcome: | Experiments on mathematical data show that proposed backward reasoning outperforms Self-Consistency. |