Papers by Zhiqiang Shen
Learning to Plan for Retrieval-Augmented Large Language Models from Knowledge Graphs (2024.findings-emnlp)
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Junjie Wang, Mingyang Chen, Binbin Hu, Dan Yang, Ziqi Liu, Yue Shen, Peng Wei, Zhiqiang Zhang, Jinjie Gu, Jun Zhou, Jeff Pan, Wen Zhang, Huajun Chen
| Challenge: | Recent studies have attempted to enhance the performance of large language models (LLMs) in complex question-answering (QA) tasks by combining step-wise planning with external retrieval. |
| Approach: | They propose a framework for enhancing LLMs’ planning capabilities by using planning data derived from knowledge graphs (KGs). |
| Outcome: | The proposed framework improves LLMs’ planning capabilities by using knowledge graphs (KGs) the proposed framework is compared with existing frameworks on multiple datasets and shows that it is effective for large language models. |
Selection and Generation: Learning towards Multi-Product Advertisement Post Generation (2020.emnlp-main)
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| Challenge: | E-commerce websites have billions of products, so it is impossible to write all copywriting manually. |
| Approach: | They propose a model to generate an AD post using a select network and a MGenNet network to generate a post including selected products. |
| Outcome: | The proposed model achieves impressive performance on a large-scale real-world AD post dataset. |
Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning (2025.acl-long)
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Zhu Xu, Zhiqiang Zhao, Zihan Zhang, Yuchi Liu, Quanwei Shen, Fei Liu, Yu Kuang, Jian He, Conglin Liu
| Challenge: | Large language models (LLMs) use tokenization methods but often obscure internal character structures within tokens. |
| Approach: | They propose a method that improves models’ ability to capture character positions within tokens by training them on reverse character prediction tasks using the tokenizer’s vocabulary. |
| Outcome: | Experiments show that the proposed method improves position prediction accuracy in large language models, enabling more precise identification of target characters in original text. |
LLMSurgeon: Diagnosing Data Mixture of Large Language Models (2026.acl-long)
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Yaxin Luo, Jiacheng Cui, Xiaohan Zhao, Xinyi Shang, Jiacheng Liu, Xinyue Bi, Zhaoyi Li, Zhiqiang Shen
| Challenge: | a lack of transparency in large language models makes auditing their "digital DNA" difficult. |
| Approach: | They propose a framework that casts DMS as an inverse problem under label-shift assumption . they propose LLMScan, a recipe-verifiable evaluation suite built from open-source LLMs . |
| Outcome: | The proposed framework casts DMS as an inverse problem under label-shift assumption . compared with existing frameworks, it recovers domain mixtures with high fidelity . |
Prompting Test-Time Scaling Is A Strong LLM Reasoning Data Augmentation (2026.findings-acl)
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| Challenge: | Large language models exhibit strong reasoning when guided by chain-of-thought exemplars . collecting large, high-quality reasoning datasets remains laborious and resource-intensive . |
| Approach: | They propose a prompt-space data augmentation framework for enhancing LLM reasoning . they use a pool of 90 randomly selected reasoning instances to elicit diverse reasoning trajectories . |
| Outcome: | The proposed framework improves accuracy over small-data benchmarks and generalization on out-of-domain reasoning evaluations. |
DRAG: Distilling RAG for SLMs from LLMs to Transfer Knowledge and Mitigate Hallucination via Evidence and Graph-based Distillation (2025.acl-long)
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Jennifer Chen, Aidar Myrzakhan, Yaxin Luo, Hassaan Muhammad Khan, Sondos Mahmoud Bsharat, Zhiqiang Shen
| Challenge: | Large-scale RAG systems consume significant computational resources and are prone to generating “hallucinated” content from Humans. |
| Approach: | They propose a framework for distilling RAG knowledge from large-scale language models into small LMs. |
| Outcome: | The proposed method outperforms the prior competitive RAG methods like MiniRAG for SLMs by up to 27.7% using the same models, preserving high-level efficiency and reliability. |
KITAB-Bench: A Comprehensive Multi-Domain Benchmark for Arabic OCR and Document Understanding (2025.findings-acl)
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Ahmed Heakl, Muhammad Abdullah Sohail, Mukul Ranjan, Rania Elbadry, Ghazi Shazan Ahmad, Mohamed El-Geish, Omar Maher, Zhiqiang Shen, Fahad Shahbaz Khan, Salman Khan
| Challenge: | Optical Character Recognition (OCR) is a key component of document processing . Arabic text recognition has complex typographic and calligraphic features . |
| Approach: | They propose a comprehensive Arabic OCR benchmark that fills the gaps in evaluation systems. |
| Outcome: | The proposed benchmark outperforms existing models in Arabic by 60% in the character error rate . the best model achieves only 65% accuracy in PDF-to-Markdown conversion . |
SeaLLMs - Large Language Models for Southeast Asia (2024.acl-demos)
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Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li, Mahani Aljunied, Zhiqiang Hu, Chenhui Shen, Yew Ken Chia, Xingxuan Li, Jianyu Wang, Qingyu Tan, Liying Cheng, Guanzheng Chen, Yue Deng, Sen Yang, Chaoqun Liu, Hang Zhang, Lidong Bing
| Challenge: | Existing large language models favor high-resource languages, such as English, at the expense of low-resourced and regional languages. |
| Approach: | They propose a series of language models that specifically focuses on Southeast Asian languages. |
| Outcome: | SeaLLM models outperform ChatGPT-3.5 in non-Latin languages by large margins . linguistic disparity impedes access to state-of-the-art AI technologies for non-English-speaking populations . |
On the Cultural Anachronism and Temporal Reasoning in Vision Language Models (2026.findings-acl)
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| Challenge: | Vision-Language Models (VLMs) are increasingly applied to cultural heritage materials. |
| Approach: | They propose a temporal anachronism benchmark to evaluate temporal reasoning on 1,600 Indian cultural artifacts. |
| Outcome: | The proposed model achieves only 58.7% accuracy on the best model, which is a significant performance gap across architectures and scales. |
FinEval: A Chinese Financial Domain Knowledge Evaluation Benchmark for Large Language Models (2025.naacl-long)
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Xin Guo, Haotian Xia, Zhaowei Liu, Hanyang Cao, Zhi Yang, Zhiqiang Liu, Sizhe Wang, Jinyi Niu, Chuqi Wang, Yanhui Wang, Xiaolong Liang, Xiaoming Huang, Bing Zhu, Zhongyu Wei, Yun Chen, Weining Shen, Liwen Zhang
| Challenge: | Large language models have demonstrated outstanding performance in various natural language processing tasks, but their security capabilities in the financial domain have not been explored. |
| Approach: | They propose to use a benchmark to evaluate large language models' financial domain knowledge and practical abilities. |
| Outcome: | The proposed benchmark evaluates large language models' financial domain knowledge and practical abilities. |