Papers by Zhiqiang Gao
AIRCoder: Adaptive Integration of Multi-dimensional Retrieval for Repository-level Code Completion (2026.acl-long)
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| Challenge: | Existing methods for retrieving code from large codebases use textual similarity or dependency existence, resulting in inconsistent performance. |
| Approach: | They propose a retrieval framework that integrates eight complementary metrics across three dimensions: textual similarity, dependency existence, and structural hierarchy. |
| Outcome: | Experiments on CrossCodeEval and RepoEval show that AIRCoder improves accuracy and performance by 10.2 over baseline methods. |
Selection and Generation: Learning towards Multi-Product Advertisement Post Generation (2020.emnlp-main)
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| Challenge: | E-commerce websites have billions of products, so it is impossible to write all copywriting manually. |
| Approach: | They propose a model to generate an AD post using a select network and a MGenNet network to generate a post including selected products. |
| Outcome: | The proposed model achieves impressive performance on a large-scale real-world AD post dataset. |
SEAVER: Attention Reallocation for Mitigating Distractions in Language Models for Conditional Semantic Textual Similarity Measurement (2024.findings-emnlp)
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| Challenge: | Conditional Semantic Textual Similarity (C-STS) introduces specific limiting conditions to the traditional Semantics task. |
| Approach: | They propose a conditional semantic textual similarity (C-STS) task that introduces specific limiting conditions to the traditional Semantic Textual Similarity task. |
| Outcome: | The proposed model outperforms existing models on the C-STS-2023 test set and consistently improves on million-scale fine-tuning baseline models (up to 3 points). |
Nested Browser-Use Learning for Agentic Information Seeking (2026.acl-long)
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Baixuan Li, Jialong Wu, Wenbiao Yin, Kuan Li, Zhongwang Zhang, Huifeng Yin, Zhengwei Tao, Liwen Zhang, Pengjun Xie, Jingren Zhou, Yong Jiang, Wentao Zhang, Zhiqiang Gao
| Challenge: | Existing information-seeking (IS) agents rely on the web for their information acquisition. |
| Approach: | They propose a browser-action framework that decouples interaction control from page exploration through a nested structure. |
| Outcome: | Empirical results show that NestBrowse offers clear benefits in practice. |
Few-Shot Semantic Dependency Parsing via Graph Contrastive Learning (2024.lrec-main)
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| Challenge: | Existing graph neural networks (GNNs) have shown promising performance on semantic dependency parsing (SDP) training a high-performing model requires a large amount of labeled data and it is prone to over-fitting in the absence of sufficient labele . |
| Approach: | They propose a syntax-guided graph contrastive learning framework to train GNNs with unlabeled data and fine-tune pre-trained GNN models with few-shot labeled SDP data. |
| Outcome: | The proposed framework achieves promising results when few-shot training samples are available. |
RASPberry: Retrieval-Augmented Monte Carlo Tree Self-Play with Reasoning Consistency for Multi-Hop Question Answering (2025.findings-acl)
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| Challenge: | Existing methods for generating and analyzing multiple document knowledge are not effective for multi-hop question answering. |
| Approach: | They propose a Monte Carlo tree-based approach to inference-time scaling using RASPberry. |
| Outcome: | Experimental results show that the proposed method achieves better inference-time scaling on smaller LLMs. |
DynGL-SDP: Dynamic Graph Learning for Semantic Dependency Parsing (2022.coling-1)
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| Challenge: | Existing parsers that learn graph representations based on static graphs are error-prone and disjointed . Graph-based parser can parse sentences efficiently but suffer from error propagation . |
| Approach: | They propose a dynamic graph learning framework to learn graph representations based on a static graph constructed by an existing parser. |
| Outcome: | The proposed parser outperforms the previous parsers on the SemEval-2015 task 18 dataset in three languages. |