Papers by Gang Zhao
CART: A Generative Cross-Modal Retrieval Framework With Coarse-To-Fine Semantic Modeling (2025.acl-long)
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Minghui Fang, Shengpeng Ji, Jialong Zuo, Hai Huang, Yan Xia, Jieming Zhu, Xize Cheng, Xiaoda Yang, Wenrui Liu, Gang Wang, Zhenhua Dong, Zhou Zhao
| Challenge: | Cross-modal retrieval tasks are used to retrieve data from one modality or another based on a query from another modality. |
| Approach: | They propose a generative cross-modal retrieval framework based on coarse-to-fine semantic modeling . they propose combining K-Means and RQ-VAE to discretize multimodal data into token sequences that support autoregressive generation. |
| Outcome: | The proposed framework achieves excellent performance and efficiency in multimodal retrieval tasks. |
A Goal Without a Plan Is Just a Wish: Efficient and Effective Global Planner Training for Long-Horizon Agent Tasks (2026.acl-long)
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Shuzheng Si, Haozhe Zhao, Kangyang Luo, Gang Chen, Fanchao Qi, Minjia Zhang, Baobao Chang, Maosong Sun
| Challenge: | Recent advances in large language models (LLMs) have leapt from static chatbots to versatile agents that tackle complex tasks such as science experiments. |
| Approach: | They propose a plan-and-execute framework and propose 'EAGLET' to enhance the executor agent's planning abilities without human effort. |
| Outcome: | The proposed method outperforms existing methods on three long-horizon tasks and reduces training costs by 8 compared to baselines. |
Learning Geometry-Aware Representations for New Intent Discovery (2024.acl-long)
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| Challenge: | Existing methods for intent classification fail to distinguish new intents due to intertwined centers . a novel framework that learns geometry-aware representations to maximally separate all intents is proposed . |
| Approach: | They propose a new intent discovery framework that learns geometry-aware representations to maximally separate all intents. |
| Outcome: | The proposed framework achieves a new state-of-the-art performance on three benchmarking datasets. |
FaithLens: Detecting and Explaining Faithfulness Hallucination (2026.findings-acl)
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Shuzheng Si, Qingyi Wang, Haozhe Zhao, Yuzhuo Bai, Guanqiao Chen, Kangyang Luo, Gang Chen, Fanchao Qi, Minjia Zhang, Baobao Chang, Maosong Sun
| Challenge: | Recent progress in large language models (LLMs) has revolutionized text generation. |
| Approach: | They propose a faithfulness hallucination detection model that can provide binary predictions and corresponding explanations to improve trustworthiness. |
| Outcome: | The proposed model outperforms advanced models on 12 diverse tasks. |
Towards Interpretable Tabular Reasoning: Enhancing LLM Reasoning on Tabular Data with Pre-Constructed Logic Graph (2026.acl-long)
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Lirong Gao, Zewei Yu, Zhongrui Yin, Qi Zhang, Yuke Zhu, Bo Zheng, Haobo Wang, Junbo Zhao, Gang Chen, Sheng Guo
| Challenge: | Tabular data is used in fields such as finance and healthcare due to its heterogeneity and complexity. |
| Approach: | They propose a Logic-Graph-Enhanced LLM Reasoning framework that integrates the strengths of tree-based models and LLMs to improve their interpretability. |
| Outcome: | The proposed framework outperforms tree-based models and state-of-the-art LLMs on tabular prediction tasks, achieving superior accuracy and interpretability. |
On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation. |
| Approach: | They propose a generic workflow for LLM-driven synthetic data generation. |
| Outcome: | The proposed workflows highlight gaps in existing research and outline avenues for future studies. |
DemoNSF: A Multi-task Demonstration-based Generative Framework for Noisy Slot Filling Task (2023.findings-emnlp)
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Guanting Dong, Tingfeng Hui, Zhuoma GongQue, Jinxu Zhao, Daichi Guo, Gang Zhao, Keqing He, Weiran Xu
| Challenge: | Recent prompt-based generative frameworks have shown impressive capabilities in sequence labeling tasks. |
| Approach: | They propose a multi-task demonstration-based generative framework for noisy slot filling that captures input perturbations at different granularities. |
| Outcome: | The proposed framework outperforms baseline methods and achieves strong generalization. |
Ensembling Prompting Strategies for Zero-Shot Hierarchical Text Classification with Large Language Models (2025.emnlp-main)
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| Challenge: | Hierarchical text classification is a challenging task in natural language processing. |
| Approach: | They propose a method which integrates the results of diverse prompting strategies to promote LLMs’ reliability. |
| Outcome: | The proposed method boosts the performance of single prompting strategies and achieves SOTA results on three benchmark datasets. |
VLASCD: A Visual Language Action Model for Simultaneous Chatting and Decision Making (2025.emnlp-main)
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| Challenge: | Recent large-scale pretrained models are built upon a multi-input single-output paradigm . tasks compete for a shared output channel, creating mutual exclusion effects . |
| Approach: | They propose a multi-input single-output (MISO) paradigm for large pretrained models . they propose unified training framework that enables concurrent multi-task outputs . |
| Outcome: | Experiments on autonomous driving platform show that MIMO-VLA outperforms state-of-the-art models in MIMO settings. |
Data Contamination Calibration for Black-box LLMs (2024.findings-acl)
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| Challenge: | Despite the rapid advancements of Large Language Models, the unchecked ultra-large-scale training sets introduce a series of potential risks like data contamination. |
| Approach: | They propose a method to detect contaminated training data and diminish the contamination effect by using a to-be-released dataset. |
| Outcome: | The proposed method outperforms existing methods by at least 4.5% on more 4 dataset formats, with more than 10 base LLMs. |
Visual Enhanced Entity-Level Interaction Network for Multimodal Summarization (2024.findings-naacl)
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| Challenge: | Existing methods to generate concise summarizations rely on coarse-grained textual and visual information, but they are underutilized. |
| Approach: | They propose a Visual Enhanced Entity-Level Interaction Network to address underutilization of multimodal inputs at a fine-grained level. |
| Outcome: | The proposed model outperforms existing models on two MMS datasets and proposes new metrics to measure factual consistency of entities in the output. |
LoRE: Enhancing Search Relevance with Progressive Chain-of-Thought and Preference Alignment (2026.findings-acl)
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Chenji Lu, Zhuo Chen, Hui Zhao, Zhiyuan Zeng, Gang Zhao, Junjie Ren, null Lihaoran, Songyan Liu, Pengjie Wang, Chuan Yu, Jian Xu, Bo Zheng
| Challenge: | E-commerce search relevance is a critical component of retrieval systems. |
| Approach: | They propose a large-generative model for search relevance that trains reasoning knowledge, multi-modal understanding and rule awareness into three core competencies. |
| Outcome: | The proposed model outperforms GPT-5 in Macro-F1 and achieves 27% online gain. |
Entity-level Interaction via Heterogeneous Graph for Multimodal Named Entity Recognition (2022.findings-emnlp)
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| Challenge: | Existing methods for name-based entity recognition neglect the integrity of entity semantics and conduct cross-modal interaction at token-level. |
| Approach: | They propose a multimodal named entity recognition model that captures visual information and fuses it into tokens to rid non-entity tokens of visual noise. |
| Outcome: | The proposed model captures entity-related visual information and fuses it into tokens . it eliminates visual noise and makes non-entity tokens easily misidentified as entities . |
Code Vulnerability Detection via Nearest Neighbor Mechanism (2022.findings-emnlp)
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| Challenge: | Existing methods to learn code semantics from source code are difficult to identify. |
| Approach: | They propose a method which retrieves multiple neighbor samples and utilizes label information to provide help for model predictions. |
| Outcome: | Extensive experiments show that the proposed method can achieve obvious performance improvements compared to baseline models. |
GUI-Bee: Align GUI Action Grounding to Novel Environments via Autonomous Exploration (2025.emnlp-main)
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| Challenge: | Recent work of GUI action grounding fine-tunes data from pre-trained MLLMs, but data is limited to specific GUI environments. |
| Approach: | They propose to use a GUI-based agent to collect environment-specific data and fine-tune GUI grounding models with the collected data. |
| Outcome: | The proposed model can be extended to other GUI environments to improve performance. |
Aligning Large Language Models to Follow Instructions and Hallucinate Less via Effective Data Filtering (2025.acl-long)
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Shuzheng Si, Haozhe Zhao, Gang Chen, Cheng Gao, Yuzhuo Bai, Zhitong Wang, Kaikai An, Kangyang Luo, Chen Qian, Fanchao Qi, Baobao Chang, Maosong Sun
| Challenge: | Existing studies show that training LLMs on data containing unfamiliar knowledge during instruction tuning can encourage hallucinations. |
| Approach: | They propose a framework that measures how familiar the LLM is with instruction data and introduce an expert-aligned reward model to ensure the quality of selected samples. |
| Outcome: | The proposed framework reduces hallucinations while maintaining a competitive ability to follow instructions. |
DemoSG: Demonstration-enhanced Schema-guided Generation for Low-resource Event Extraction (2023.findings-emnlp)
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| Challenge: | Current Event Extraction methods focus on high-resource scenarios, which requires large amount of annotated data. |
| Approach: | They propose a demonstration-based learning paradigm for EE to fully use annotated data . they propose EE as a natural language generation task guided by schema-based prompts . |
| Outcome: | The proposed model outperforms current methods in low-resource scenarios. |
Joint Geometrical and Statistical Domain Adaptation for Cross-domain Code Vulnerability Detection (2023.emnlp-main)
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| Challenge: | Existing approaches to detect code vulnerability are limited by labeled training data on target domains. |
| Approach: | They propose a cross-domain code vulnerability detection framework called MNCRI . they propose mutual nearest neighbor contrastive learning to align the source and target domains . |
| Outcome: | The proposed framework outperforms state-of-the-art methods in cross-domain code vulnerability detection tasks. |
FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models (2023.emnlp-main)
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| Challenge: | Modern machine learning models require a huge collection of precisely labeled data, which can be labor-intensive and time-consuming. |
| Approach: | They propose a collaborative learning framework that interactively distills and filters the task-specific knowledge from LLMs. |
| Outcome: | The proposed framework improves zero-shot performance on eight benchmark datasets without human supervision. |
GATEAU: Selecting Influential Samples for Long Context Alignment (2025.emnlp-main)
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Shuzheng Si, Haozhe Zhao, Gang Chen, Yunshui Li, Kangyang Luo, Chuancheng Lv, Kaikai An, Fanchao Qi, Baobao Chang, Maosong Sun
| Challenge: | Existing studies have attempted to scale up the available data volume by synthesizing long instruction-following samples, but a lack of a well-defined strategy for ensuring data quality may introduce low-quality samples and restrict the model’s performance. |
| Approach: | They propose a framework to identify influential samples enriched with long-range dependency relations that can be used to align large language models to handle instructions with extremely long contexts. |
| Outcome: | The proposed framework identifies samples with long-range dependency relations and shows that the model trained on these samples exhibits better instruction-following and long-context understanding capabilities. |
LongTableBench: Benchmarking Long-Context Table Reasoning across Real-World Formats and Domains (2025.findings-emnlp)
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Liyao Li, Jiaming Tian, Hao Chen, Wentao Ye, Chao Ye, Haobo Wang, Ningtao Wang, Xing Fu, Gang Chen, Junbo Zhao
| Challenge: | Evaluating 52 LLMs reveals that only the strongest models maintain robust performance under increasing context lengths and format diversity. |
| Approach: | They propose a benchmark for evaluating long-context reasoning over semi-structured tables across diverse formats, tasks, and domains. |
| Outcome: | The proposed model outperforms compression-based approaches on tasks requiring semantic integration. |
Embedding and Gradient Say Wrong: A White-Box Method for Hallucination Detection (2024.emnlp-main)
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| Challenge: | Existing methods for hallucination detection have attracted more attention from the community. |
| Approach: | They propose to model the distributional distance between the regular conditional output and the unconditional output, which is generated without a given input text. |
| Outcome: | The proposed model achieves state-of-the-art on the hallucination benchmarks HADES and other datasets. |
Wav2SQL: Direct Generalizable Speech-To-SQL Parsing (2024.findings-acl)
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| Challenge: | Existing models for speech-driven SQL parsing are based on a cascaded approach, resulting in data scarcity and inconsistent performance. |
| Approach: | They propose a direct generalizable speech-to-SQL parsing model which avoids error compounding across cascaded systems. |
| Outcome: | The proposed model avoids error compounding and achieves state-of-the-art results by 4.7% improvement over baseline. |
CYCLE-INSTRUCT: Fully Seed-Free Instruction Tuning via Dual Self-Training and Cycle Consistency (2025.emnlp-main)
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Zhanming Shen, Hao Chen, Yulei Tang, Shaolin Zhu, Wentao Ye, Xiaomeng Hu, Haobo Wang, Gang Chen, Junbo Zhao
| Challenge: | Existing methods for instruction tuning rely on expensive human-annotated seed data or powerful external teacher models. |
| Approach: | They propose a framework that achieves fully seed-free instruction tuning by employing a dual self-training loop where two models are bootstrapped solely from raw, unlabeled text. |
| Outcome: | The proposed framework outperforms seed-driven back-translation baselines and achieves comparable performance to strongly supervised methods. |