Papers by Haoran Ding
Temporal Scaling Law for Large Language Models (2025.emnlp-main)
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Yizhe Xiong, Xiansheng Chen, Xin Ye, Hui Chen, Zijia Lin, Haoran Lian, Zhenpeng Su, Wei Huang, Jianwei Niu, Jungong Han, Guiguang Ding
| Challenge: | Existing studies have found that the test loss of LLMs scales as power-laws with model size, computational budget, and dataset size. |
| Approach: | They propose a concept of Temporal Scaling Law to study test loss of LLMs . they break down test loss into fine-grained token positions and develop a dynamic hyperbolic-law . |
| Outcome: | The proposed model predicts the test loss of LLMs as the training steps scale up. |
On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions (2025.findings-emnlp)
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Weiqi Wang, Tianqing Fang, Haochen Shi, Baixuan Xu, Wenxuan Ding, Liyu Zhang, Wei Fan, Jiaxin Bai, Haoran Li, Xin Liu, Yangqiu Song
| Challenge: | Conceptualization is a fundamental element of human cognition and plays a pivotal role in generalizable reasoning. |
| Approach: | They propose to categorize different types of conceptualizations into four levels based on the types of instances being conceptualized. |
| Outcome: | The proposed categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized . |
Tree-Structured Topic Modeling with Nonparametric Neural Variational Inference (2021.acl-long)
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| Challenge: | Existing methods for topic modeling learn topics with a flat structure . however, such methods have data scalability issues . |
| Approach: | They propose to use nonparametric neural variational inference to extract a tree-structured topic model with reasonable structure, low redundancy, and adaptable widths. |
| Outcome: | The proposed model extracts a tree-structured topic hierarchy with reasonable structure, low redundancy, and adaptable widths. |
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions (2021.emnlp-main)
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| Challenge: | Keyword or keyphrase extraction is to identify words or phrases presenting the main topics of a document. |
| Approach: | They propose a hybrid attention model to identify keyphrases from a document in an unsupervised manner. |
| Outcome: | The proposed model is effective and robust on long and short documents. |
AGRank: Augmented Graph-based Unsupervised Keyphrase Extraction (2022.aacl-main)
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| Challenge: | Unsupervised keyphrase extraction (UKE) is highly anticipated because no labeled data is needed to train a model. |
| Approach: | They propose an augmented graph-based unsupervised model to identify keyphrases from a document by integrating graph and deep learning methods. |
| Outcome: | The proposed model is effective and robust for long and short documents. |
Adaptive Bridge between Training and Inference for Dialogue Generation (2021.emnlp-main)
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| Challenge: | Experimental results show that our model can achieve a significant improvement in terms of metric-based evaluation and human evaluation compared with the state-of-the-art exposure bias approaches. |
| Approach: | They propose a novel adaptive switching mechanism which automatically transits between ground-truth learning and generated learning regarding the word-level matching score. |
| Outcome: | The proposed model improves on Chinese and English reddit datasets compared with state-of-the-art models on the word-level matching score. |
COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values (2026.findings-eacl)
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Siwei Wu, JinCheng Ren, Xeron Du, Shuyue Guo, Xingwei Qu, Yiming Liang, Jie Liu, Yunwen Li, Tyler Loakman, Tianyu Zheng, Boyu Feng, Huaqing Yuan, Zili Wang, Jiaheng Liu, Wenhao Huang, Chenglin Cai, Haoran Que, Jian Yang, Yuelin Bai, Zekun Moore Wang, Zhouliang Yu, Qunshu Lin, Ding Pan, Yuchen Eleanor Jiang, Tiannan Wang, Wangchunshu Zhou, Shenzhi Wang, Xingyuan Bu, Minghao Liu, Guoyin Wang, Ge Zhang, Chenghua Lin
| Challenge: | Existing Chinese preference datasets suffer from limited scale, restricted domain coverage, and insufficiently rigorous data validation. |
| Approach: | They propose an LLM-based data annotation pipeline with no human intervention to annotate Chinese preference datasets. |
| Outcome: | The proposed pipeline outperforms existing Chinese preference datasets on AlignBench and Chinese Reward Benchmark. |
Breaking the Stage Barrier: A Novel Single-Stage Approach to Long Context Extension for Large Language Models (2025.coling-main)
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Haoran Lian, Junmin Chen, Wei Huang, Yizhe Xiong, Wenping Hu, Guiguang Ding, Hui Chen, Jianwei Niu, Zijia Lin, Fuzheng Zhang, Di Zhang
| Challenge: | Recent studies show that Large language models struggle with handling long token sequences due to limited training context size. |
| Approach: | They propose a single-stage continual pretraining method to equip LLMs with long context modeling capabilities. |
| Outcome: | The proposed method outperforms existing methods on 4 language modeling benchmarks. |
A Data-Centric Framework for Composable NLP Workflows (2020.emnlp-demos)
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Zhengzhong Liu, Guanxiong Ding, Avinash Bukkittu, Mansi Gupta, Pengzhi Gao, Atif Ahmed, Shikun Zhang, Xin Gao, Swapnil Singhavi, Linwei Li, Wei Wei, Zecong Hu, Haoran Shi, Xiaodan Liang, Teruko Mitamura, Eric Xing, Zhiting Hu
| Challenge: | Empirical natural language processing (NLP) systems involve interoperation among multiple components . a wealth of NLP toolkits exist ( 4), such as spaCy, DKPro, CoreNLP. |
| Approach: | They propose a unified open-source framework that supports fast development of NLP workflows . framework includes processors for NLP tasks, visualization, and annotation . |
| Outcome: | The framework offers processors for NLP tasks, visualization, and annotation, and is extensible . it is delivered through two modularized yet integratable open-source projects, Forte and Stave . |