Papers by Yangyang Guo
REPT: Bridging Language Models and Machine Reading Comprehension via Retrieval-Based Pre-training (2021.findings-acl)
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| Challenge: | Pre-trained language models have achieved great success on Machine Reading Comprehension (MRC) however, the poor support in evidence extraction hinders them from further advancing MRC. |
| Approach: | They propose a REtrieval-based pre-training approach that strengthens evidence extraction during pre-training by inherited downstream MRC tasks. |
| Outcome: | The proposed approach strengthens evidence extraction during pre-training, which is further inherited by downstream tasks. |
MERIt: Meta-Path Guided Contrastive Learning for Logical Reasoning (2022.findings-acl)
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| Challenge: | Existing methods to infer logical relations with annotated training data suffer from over-fitting and poor generalization problems due to the dataset sparsity. |
| Approach: | They propose a MEta-path guided contrastive learning method for logical ReasonIng of text that performs self-supervised pre-training on abundant unlabeled text data. |
| Outcome: | The proposed method outperforms the baselines on two logical reasoning benchmarks with significant improvements. |
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 . |
Efficient Prompting for Continual Adaptation to Missing Modalities (2025.naacl-long)
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| Challenge: | Existing methods combine various missing cases to train recovery modules or align multimodal features, resulting in suboptimal performance, high computational costs, and catastrophic forgetting. |
| Approach: | They propose a continual multimodal missing modality task that uses prompts to learn modalities . existing methods often aggregate various missing cases to train recovery modules . authors conduct extensive experiments on three public datasets . |
| Outcome: | The proposed method consistently outperforms state-of-the-art methods on three public datasets. |