Papers by Yulong Pei
DocLLM: A Layout-Aware Generative Language Model for Multimodal Document Understanding (2024.acl-long)
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Dongsheng Wang, Natraj Raman, Mathieu Sibue, Zhiqiang Ma, Petr Babkin, Simerjot Kaur, Yulong Pei, Armineh Nourbakhsh, Xiaomo Liu
| Challenge: | Documents with rich layouts are a significant portion of enterprise corpora and document AI is still a challenge. |
| Approach: | They propose a lightweight extension to traditional large language models for reasoning over visual documents that takes into account both textual semantics and spatial layout. |
| Outcome: | The proposed model outperforms existing large language models on 14 out of 16 datasets and generalizes well to 4 out of 5 previously unseen datasets. |
Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks (2023.emnlp-industry)
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| Challenge: | Recent large language models such as ChatGPT and GPT-4 have shown exceptional capabilities of generalist models . however, their applicability and effectiveness in specific domains like finance needs a better understanding . |
| Approach: | They conduct empirical studies to compare the performance of ChatGPT and GPT-4 on financial text analytical problems using eight benchmark datasets from five categories of tasks. |
| Outcome: | The proposed models outperform the state-of-the-art models on a wide range of financial text analytical tasks. |
More than Minorities and Majorities: Understanding Multilateral Bias in Language Generation (2024.findings-acl)
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| Challenge: | Existing studies on bias dataset construction and mitigation focus on one demographic group . in real-world applications, there are more than two demographic groups at risk of the same bias. |
| Approach: | They propose to analyze and reduce biases across multiple demographic groups using a multi-demographic bias dataset. |
| Outcome: | The proposed method can mitigate biases among multiple demographic groups effectively, the authors show . |