Papers by Weishi Wang
Towards Low-Resource Automatic Program Repair with Meta-Learning and Pretrained Language Models (2023.emnlp-main)
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| Challenge: | Recent advances in deep learning (DL) based APR models have demonstrated promising results by learning from large-scale bug-fix examples in a data-driven manner. |
| Approach: | They propose a meta-learning framework integrated with code pretrained language models to generate fixes for low-resource bugs with limited training samples. |
| Outcome: | The proposed framework learns better error-specific knowledge from high-resource bugs through efficient first-order meta-learning optimization, which allows for a faster adaptation to the target low-resourced bugs. |
Rethinking Reading Order: Toward Generalizable Document Understanding with LLM-based Relation Modeling (2026.eacl-long)
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| Challenge: | Document understanding requires modeling structural and semantic relationships between layout elements within the document without human supervision. |
| Approach: | They propose a cost-effective paradigm that leverages large language models to infer global RO and inter-element layout relations without human supervision. |
| Outcome: | Experiments on Semantic Entity Recognition, Entity Linking, and Document Question Answering show that the proposed model improves on baseline models while preserving the robustness of existing models. |
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. |
CityCube: Benchmarking Cross-view Spatial Reasoning on Vision-Language Models in Urban Environments (2026.acl-long)
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Haotian Xu, Yue Hu, Zhengqiu Zhu, Chen Gao, Ziyou Wang, Junreng Rao, Wenhao Lu, Weishi Li, Quanjun Yin, Yong Li
| Challenge: | Existing benchmarks focus on indoor or street settings, overlooking challenges of open-ended urban spaces. |
| Approach: | They propose a benchmark to probe cross-view spatial reasoning capabilities of current VLMs in urban settings. |
| Outcome: | The citycube benchmark examines the performance of current vision-language models in urban environments. |
XCodeEval: An Execution-based Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval (2024.acl-long)
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| Challenge: | Recent advances in large language models have shown impressive abilities in generating codes from natural language descriptions, repairing buggy codes, translating codes between languages, and retrieving relevant code segments. |
| Approach: | They propose to use a multilingual multitask benchmark to evaluate large language models that can generate codes from natural language descriptions, repair buggy codes, and translate between languages. |
| Outcome: | The proposed model performs 7 tasks covering up to 11 languages with execution-level parallelism and 25 M document-level coding examples (16.5 B tokens) |
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation (2021.emnlp-main)
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| Challenge: | Pre-trained models for Natural Languages (NL) like BERT and GPT have been shown to transfer well to Programming Languages. |
| Approach: | They propose a unified pre-trained encoder-decoder Transformer model that leverages the code semantics conveyed from the developer-assigned identifiers. |
| Outcome: | The proposed model outperforms existing models on understanding and generation tasks and can capture semantic information from code. |
Response Selection for Multi-Party Conversations with Dynamic Topic Tracking (2020.emnlp-main)
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| Challenge: | Existing response selection methods focus on a two-party single-conversation scenario. |
| Approach: | They propose a multi-task learning framework that frames response selection as a dynamic topic tracking task to match the topic between the response and relevant conversation context. |
| Outcome: | The proposed framework outperforms existing methods on an Ubuntu IRC dataset in response selection and topic disentanglement tasks. |