Papers by Yijia Zhao
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 . |
AgentReview: Exploring Peer Review Dynamics with LLM Agents (2024.emnlp-main)
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| Challenge: | Existing methods of peer review analysis do not address multivariate nature of the process, account for latent variables, and are constrained by privacy concerns due to the sensitive nature of data. |
| Approach: | They propose a large language model based peer review simulation framework which effectively disentangles the impacts of multiple latent factors and addresses the privacy issue. |
| Outcome: | The proposed framework disentangles the impacts of multiple latent factors and addresses privacy concerns. |
Distilling Knowledge for Search-based Structured Prediction (P18-1)
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| Challenge: | Existing studies have focused on the performance of structured prediction models, but they are often limited by the ambiguities of the reference policy. |
| Approach: | They propose to distill an ensemble of multiple models trained with different initializations into a single model and use it to explore the search space. |
| Outcome: | The proposed model outperforms the greedy models on two typical search-based structured prediction tasks and achieves 1.32 in LAS and 2.65 in BLEU over strong baselines. |
Class-Incremental Learning based on Label Generation (2023.acl-short)
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| Challenge: | Existing studies on pre-trained language models focus on task-incremental learning (TIL) but they perform poorly in a more challenging setting of class-incremental learning. |
| Approach: | They propose a method which solves CIL based on label generation by using sparse vocabulary and creates pseudo-replay samples by using label semantics. |
| Outcome: | The proposed method outperforms baseline models by a large margin in the class-incremental learning setting. |
Protein Large Language Models: A Comprehensive Survey (2025.findings-emnlp)
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Yijia Xiao, Wanjia Zhao, Junkai Zhang, Yiqiao Jin, Han Zhang, Zhicheng Ren, Renliang Sun, Haixin Wang, Guancheng Wan, Pan Lu, Xiao Luo, Yu Zhang, James Zou, Yizhou Sun, Wei Wang
| Challenge: | Existing studies focus on specific aspects or applications, but this study provides a comprehensive overview of Protein-specific large language models. |
| Approach: | This paper proposes a structured taxonomy of state-of-the-art ProteinLLMs . they analyze how they leverage large-scale protein sequence data for improved accuracy . |
| Outcome: | The proposed model covers their architectures, training datasets, evaluation metrics, and diverse applications. |
Large Language Models Can Be Contextual Privacy Protection Learners (2024.emnlp-main)
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Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, Wenchao Yu, Xujiang Zhao, Yanchi Liu, Quanquan Gu, Haifeng Chen, Wei Wang, Wei Cheng
| Challenge: | Large Language Models (LLMs) have demonstrated remarkable linguistic comprehension and generation capability, but when applied to specialized industries, they face challenges such as hallucination, insufficient domain knowledge, and failing to incorporate the latest domain knowledge. |
| Approach: | They propose a paradigm for fine-tuning LLMs that effectively injects domain-specific knowledge while safeguarding inference-time data privacy. |
| Outcome: | The proposed model protects private data while enhancing the model's knowledge. |
Geneverse: A Collection of Open-source Multimodal Large Language Models for Genomic and Proteomic Research (2024.findings-emnlp)
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| Challenge: | generative Large Language Models (LLMs) are a promising tool for biomedical and healthcare research. |
| Approach: | They propose to use finetuned LLMs and multimodal LLM for genomic and proteomics tasks. |
| Outcome: | The proposed models outperform closed-source models in genomic and proteomics tasks and are highly accurate. |
Lattice-BERT: Leveraging Multi-Granularity Representations in Chinese Pre-trained Language Models (2021.naacl-main)
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| Challenge: | Pre-trained language models process text as a sequence of characters, ignoring more coarse granularity, e.g., words. |
| Approach: | They propose a new pre-training paradigm for Chinese that incorporates word representations along with characters and can model a sentence in a multi-granular manner. |
| Outcome: | The proposed model can bring an average increase of 1.5% under the 12-layer setting, which achieves new state-of-the-art among base-size models on the CLUE benchmarks. |
SpecCoT: Accelerating Chain-of-Thought Reasoning through Speculative Exploration (2025.findings-emnlp)
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| Challenge: | Large Reasoning Models suffer from high inference latency due to lengthy reasoning chains. |
| Approach: | They propose a collaborative framework that combines large and small models for effective reasoning. |
| Outcome: | The proposed framework reduces inference latency by 1.7-4.1 while maintaining comparable accuracy to standard large model inference. |