Papers by Zan Wang
APIRecX: Cross-Library API Recommendation via Pre-Trained Language Model (2021.emnlp-main)
Copied to clipboard
| Challenge: | API recommendation tools can help programmers use APIs by recommending which APIs to be used next given the APIs that have been written. |
| Approach: | They propose a cross-library API recommendation approach that uses BPE to split API calls in each sequence and pre-train a GPT based language model. |
| Outcome: | The proposed APIRecX can recommend APIs that are previously regarded as OOV . it can migrate knowledge of existing libraries to a new library and recommend API that is previously viewed as OVO . |
When Language Model Meets Private Library (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Existing language models have been pre-trained on large-scale code corpora and generate decent code snippets. |
| Approach: | They propose a framework that can provide pre-trained language models with the ability to generate code using private libraries. |
| Outcome: | The proposed framework can generate code using private libraries using off-the-shelf language models or pre-trained models on code corpus containing API information. |
Large-Scale Relation Learning for Question Answering over Knowledge Bases with Pre-trained Language Models (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing KBQA methods focus on the natural language but ignore textual information carried by the nodes and edges. |
| Approach: | They propose to perform relation extraction, relation matching, and relation reasoning tasks to align the natural language expressions to the relations in the KB and reason over the missing connections. |
| Outcome: | Experiments on WebQSP show that the proposed model outperforms baselines even when the KB is incomplete. |
Large Language Models Meet NL2Code: A Survey (2023.acl-long)
Copied to clipboard
Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Wang Yongji, Jian-Guang Lou
| Challenge: | generating code from a natural language description is a pressing and significant challenge in code intelligence. |
| Approach: | They propose to survey 27 existing large language models for NL2Code and compare them to humanEval benchmarks. |
| Outcome: | The proposed model is compared with existing models on the HumanEval benchmark. |
AutoProteinEngine: A Large Language Model Driven Agent Framework for Multimodal AutoML in Protein Engineering (2025.coling-industry)
Copied to clipboard
| Challenge: | Deep learning models are often inefficient and resource-intensive for biologists without specialized computational expertise. |
| Approach: | They propose an agent framework that leverages large language models for multimodal automated machine learning (AutoML) in protein engineering. |
| Outcome: | The proposed framework demonstrates significant improvements in performance over previous approaches in two real-world protein engineering tasks. |
LLMs as Bridges: Reformulating Grounded Multimodal Named Entity Recognition (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for Grounded Multimodal Named Entity Recognition (GMNER) lack a strong correlation between image-text pairs and is ungroundable. |
| Approach: | They propose a framework that reformulates GMNER into a joint MNER-VE-VG task by leveraging large language models as a connecting bridge. |
| Outcome: | The proposed framework outperforms state-of-the-art methods on the existing GMNER dataset and achieves absolute leads of 10.65%, 6.21%, and 8.83% in all three subtasks. |
CodeV: Issue Resolving with Visual Data (2025.findings-acl)
Copied to clipboard
Linhao Zhang, Daoguang Zan, Quanshun Yang, Zhirong Huang, Dong Chen, Bo Shen, Tianyu Liu, Yongshun Gong, Huang Pengjie, Xudong Lu, Guangtai Liang, Lizhen Cui, Qianxiang Wang
| Challenge: | Large Language Models (LLMs) have expanded to more complex repository-level tasks. |
| Approach: | They propose a first approach to leveraging visual data to enhance the issue-resolving capabilities of Large Language Models (LLMs) they demonstrate the effectiveness of CodeV and provide valuable insights into leveraging visualization to resolve GitHub issues. |
| Outcome: | The proposed approach improves the issue-resolving capabilities of Large Language Models (LLMs) by using visual data. |
CodeM: Less Data Yields More Versatility via Ability Matrix (2024.findings-acl)
Copied to clipboard
Daoguang Zan, Ailun Yu, Wei Liu, Bo Shen, Shaoxin Lin, Yongshun Gong, Yafen Yao, Yan Liu, Bei Guan, Weihua Luo, Yongji Wang, Qianxiang Wang, Lizhen Cui
| Challenge: | Recent efforts to train code large language models have been booming recently . however, this will incur significant costs in constructing data and training model considering the countless downstream scenarios. |
| Approach: | They propose a data construction strategy which decouples code LLMs’ abilities into two dimensions and constructs a lightweight training corpus that only covers a subset of target scenarios. |
| Outcome: | The proposed model can train a multilingual multitasking model using less data and training data. |