Papers by Xuan Long Do
Aligning Large Language Models with Human Opinions through Persona Selection and Value–Belief–Norm Reasoning (2025.coling-main)
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| Challenge: | Current methods for reasoning and predicting human opinions employ role-playing with personae but face two major issues: LLMs are sensitive to even a single irrelevant persona, skewing predictions by up to 30%; and LLM fail to reason strategically over personas. |
| Approach: | They propose a four-step solution modeling which and how to reason with personae, inspired by the Value–Belief–Norm theory. |
| Outcome: | The proposed model improves existing methods by up to 4% by fine-tuning them with COO's data. |
UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning (2023.emnlp-main)
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| Challenge: | Existing methods for chart-based data analysis neglect explicit modeling of chart structures. |
| Approach: | They propose a pretrained model for chart comprehension and reasoning that encodes relevant text, data, and visual elements of charts and uses a chart-grounded text decoder for text generation. |
| Outcome: | The proposed model outperforms existing methods that lack explicit modeling of chart structures and lacks explicit modeling. |
Retrieving Multimodal Information for Augmented Generation: A Survey (2023.findings-emnlp)
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Ruochen Zhao, Hailin Chen, Weishi Wang, Fangkai Jiao, Xuan Long Do, Chengwei Qin, Bosheng Ding, Xiaobao Guo, Minzhi Li, Xingxuan Li, Shafiq Joty
| Challenge: | Large Language Models (LLMs) are increasingly using multimodality to augment their generation ability, but there is no unified perception of at which stage and how to incorporate different modalities. |
| Approach: | They propose to use multimodality to augment Large Language Models (LLMs) this will provide scholars with a deeper understanding of the methods' applications and encourage them to adapt existing techniques to the fast-growing field of LLMs. |
| Outcome: | The proposed methods improve factuality, reasoning, interpretability, and robustness of the generated content. |
ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning (2022.findings-acl)
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| Challenge: | Existing datasets that focus on complex reasoning questions do not address such questions as they are template-based and answers come from a fixed-vocabulary. |
| Approach: | They propose a large-scale benchmark that uses visual and logical reasoning to answer questions using a transformer-based model. |
| Outcome: | The proposed models achieve state-of-the-art on the previous datasets and on the current one, but also show that they have several challenges in answering complex reasoning questions. |
Modeling What-to-ask and How-to-ask for Answer-unaware Conversational Question Generation (2023.acl-long)
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| Challenge: | Existing methods to generate conversational question are naive and do not account for the answer span. |
| Approach: | They propose a framework for generating a conversational question from a context. |
| Outcome: | The proposed framework achieves state-of-the-art in two different settings compared to existing models . it uses a sentence as the rationale and extracts the answer span from it . |
OpenCQA: Open-ended Question Answering with Charts (2022.emnlp-main)
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| Challenge: | OpenCQA is a task to answer open-ended questions about charts with descriptive texts. |
| Approach: | They propose a task to answer open-ended questions about charts with descriptive texts. |
| Outcome: | The proposed task is to answer an open-ended question about a chart with descriptive texts. |
CoHS-CQG: Context and History Selection for Conversational Question Generation (2022.coling-1)
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| Challenge: | Existing studies focus on single-turn question generation, but few studies have studied the challenges of multiturn QG. |
| Approach: | They propose a two-stage conversational question generation framework that shortens the context and history of the input and calculates relevance scores. |
| Outcome: | The proposed framework achieves state-of-the-art on CoQA in answer-aware and answer-unaware settings. |