Papers by Shanshan Zhang
Regular Expression Guided Entity Mention Mining from Noisy Web Data (D18-1)
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| Challenge: | Named Entity Recognition (NER) is a subtask of the broader problem of Information Extraction (IE) from text. |
| Approach: | They propose a framework that uses Regular Expressions to identify entities from web data . they combine expressive power of REs with ability of deep learning to learn from large data a human expert is asked to label a small set of documents . |
| Outcome: | The proposed framework achieves impressive accuracy while requiring modest human effort. |
Synthesizing and Adapting Error Correction Data for Mobile Large Language Model Applications (2025.acl-industry)
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| Challenge: | Recent advances in large language models (LLMs) have achieved impressive performance on many language tasks. |
| Approach: | They synthesize a high-quality dataset of error correction pairs to evaluate and improve LLMs for mobile applications by reweighting the sample. |
| Outcome: | The proposed model improves on offline evaluation and live A/B testing, given the LLM performance on offline data and scores from a small privacy-preserving on-device language model. |
MultiFinBen: Benchmarking Large Language Models for Multilingual and Multimodal Financial Application (2026.acl-long)
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Xueqing Peng, Lingfei Qian, Yan Wang, Ruoyu Xiang, Yueru He, Yang Ren, Mingyang Jiang, Vincent Jim Zhang, Yuqing Guo, Jeff Zhao, Huan He, Yi Han, Yun Feng, Yuechen Jiang, Yupeng Cao, Haohang Li, Yangyang Yu, Xiaoyu Wang, Penglei Gao, Shengyuan Lin, Keyi Wang, Shanshan Yang, Yilun Zhao, Zhiwei Liu, Peng Lu, Jerry Huang, Suyuchen Wang, Triantafillos Papadopoulos, Polydoros Giannouris, Efstathia Soufleri, Nuo Chen, Zhiyang Deng, Heming Fu, Yijia Zhao, Mingquan Lin, Meikang Qiu, Kaleb E Smith, Arman Cohan, Xiao-Yang Liu, Jimin Huang, Guojun Xiong, Alejandro Lopez-Lira, Xi Chen, Junichi Tsujii, Jian-Yun Nie, Sophia Ananiadou, Qianqian Xie
| Challenge: | Existing evaluations of LLMs in finance are text-only, monolingual, and largely saturated by current models. |
| Approach: | They propose a multilingual and multimodal benchmark for evaluating LLMs in real financial contexts. |
| Outcome: | The first expert-annotated multilingual and multimodal benchmark is released . it evaluates 21 leading LLMs and shows they perform better in multilingual settings . |
Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understanding (2026.acl-long)
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Sensen Gao, Shanshan Zhao, Xu Jiang, Lunhao Duan, Yong Xien Chng, Qing-Guo Chen, Weihua Luo, Kaifu Zhang, Jia-Wang Bian, Mingming Gong
| Challenge: | Document understanding is critical for applications from financial analysis to scientific discovery. |
| Approach: | They propose a taxonomy based on domain, retrieval modality, and granularity and review advances involving graph structures and agentic frameworks. |
| Outcome: | The proposed model enables holistic retrieval and reasoning across all modalities, unlocking comprehensive document intelligence. |
QueryAgent: A Reliable and Efficient Reasoning Framework with Environmental Feedback based Self-Correction (2024.acl-long)
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| Challenge: | Existing methods for semantic parsing fail when hallucinations are encountered . QueryAgent solves a question step-by-step and performs stepwise self-correction . |
| Approach: | They propose a framework that solves a query step-by-step and performs stepwise self-correction. |
| Outcome: | The proposed framework outperforms existing methods on GrailQA and GraphQ by 5.7 and 15.0 points. |
Improving Word Embeddings through Iterative Refinement of Word- and Character-level Models (2020.coling-main)
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| Challenge: | Embedding of rare and out-of-vocabulary words is an important open NLP problem . standard embedding models are not useful for recommending jobs to users with rare or unseen words . |
| Approach: | They propose to train a character-level neural network to reproduce word embeddings . they then use the model to assign vectors to any input string, including rare words . |
| Outcome: | The proposed method outperforms existing methods on word similarity data sets and can be applied to job title normalization in the e-recruitment domain. |
Who Wrote This Line? Evaluating the Detection of LLM-Generated Classical Chinese Poetry (2026.acl-long)
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Jiang Li, Tian Lan, Shanshan Wang, Dongxing Zhang, Dianqing Lin, Guanglai Gao, Derek F. Wong, Xiangdong Su
| Challenge: | a recent study shows that large language models can generate text, but they can also fabricate large amounts of false or misleading content. |
| Approach: | They propose a benchmark to detect LLM-generated classical Chinese poetry . they compare 12 different AI detectors to find out whether a poem is authored by AI . |
| Outcome: | The proposed benchmark compared 12 AI detectors with a dataset of 30,664 Chinese poems . the results highlight the limitations of current Chinese text detectors . |