Papers by Bowen Zhao
OMS: On-the-fly, Multi-Objective, Self-Reflective Ad Keyword Generation via LLM Agent (2025.emnlp-main)
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| Challenge: | Keyword decision in Sponsored Search Advertising is critical to the success of ad campaigns. |
| Approach: | They propose a keyword generation framework that is On-the-fly and Multi-objective to automate keyword generation. |
| Outcome: | Experiments show that OMS outperforms existing methods in keyword generation . relying on large-scale query-keyword data is a major limitation, authors say . |
SGG: Learning to Select, Guide, and Generate for Keyphrase Generation (2021.naacl-main)
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| Challenge: | Existing keyphrase generation approaches synchronously generate present and absent keyphrases without explicitly distinguishing these two categories. |
| Approach: | They propose to deal with present and absent keyphrases separately with different mechanisms by using a hierarchical neural network with a pointing-based selector and a selection-guided generator. |
| Outcome: | The proposed model outperforms baselines on four keyphrase generation tasks and shows extensibility in natural language generation tasks. |
Texar: A Modularized, Versatile, and Extensible Toolkit for Text Generation (P19-3)
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Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wanrong Zhu, Devendra Sachan, Eric Xing
| Challenge: | Texar is an open-source text generation toolkit that supports a broad set of text generation tasks. |
| Approach: | They introduce Texar, an open-source text generation toolkit that supports text generation tasks. |
| Outcome: | Texar supports machine translation, summarization, dialog, content manipulation, and more. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
GenPilot: A Multi-Agent System for Test-Time Prompt Optimization in Image Generation (2025.findings-emnlp)
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Wen Ye, Zhaocheng Liu, Gui Yuwei, Tingyu Yuan, Yunyue Su, Bowen Fang, Chaoyang Zhao, Qiang Liu, Liang Wang
| Challenge: | Existing methods for text-to-image synthesis lack systematic error analysis and refinement strategies, resulting in limited reliability and effectiveness. |
| Approach: | They propose a plug-and-play multi-agent system called GenPilot that integrates error analysis, clustering-based adaptive exploration, fine-grained verification and a memory module for iterative optimization. |
| Outcome: | The proposed method improves text consistency and structural coherence on images with a plug-and-play system. |
Semi-Supervised Lifelong Language Learning (2022.findings-emnlp)
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Yingxiu Zhao, Yinhe Zheng, Bowen Yu, Zhiliang Tian, Dongkyu Lee, Jian Sun, Yongbin Li, Nevin L. Zhang
| Challenge: | Existing methods to learn languages only focus on supervised learning, and unlabeled data is underexplored. |
| Approach: | They propose a semi-supervised lifelong language learning setting where a model learns sequentially arriving language tasks with both labeled and unlabeled data. |
| Outcome: | The proposed model outperforms baseline models on various language tasks and is effective and superior to existing models. |
Fusing Highly Specialized Language Models for Comprehensive Expertise (2025.acl-long)
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Ning Ding, Yulin Chen, Ganqu Cui, Xingtai Lv, Weilin Zhao, Kaiyan Zhang, Ruobing Xie, Bowen Zhou, Zhiyuan Liu, Maosong Sun
| Challenge: | Existing models that focus on language, programming code, and mathematical symbols are not able to achieve mastery of all three domains simultaneously. |
| Approach: | They propose to fuse highly-specialized models that are already sufficiently trained on different domains to achieve a highly-specific model. |
| Outcome: | The proposed model could achieve mastery of the three crucial domains simultaneously. |
PLAWBENCH: A Rubric-Based Benchmark for Evaluating LLMs in Real-World Legal Practice (2026.acl-long)
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Yuzhen Shi, Huanghai Liu, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang, Li Zhang, Qingjing Chen, Feng Di, Wenbo Lv, Weiheng Wu, Kexin Yang, Sen Yang, Wei Wang, Rongyao Shi, Qiu Yuanyang, Yuemeng Qi, Zhang Jingwen, Sui Xiaoyu, Yifan Chen, Zhang Yi, An Yang, Bowen Yu, Dayiheng Liu, Junyang Lin, Weixing Shen, Bing Zhao, Charles L. A. Clarke, HU Wei
| Challenge: | Existing benchmarks for large language models (LLMs) are coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. |
| Approach: | They propose a Practical Law Benchmark to evaluate large language models in real-world legal practice scenarios. |
| Outcome: | The proposed model is based on 850 questions and 13 scenarios with expert-designed evaluation rubrics. |
Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training (2020.coling-main)
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| Challenge: | Existing few-shot relation classifiers struggle to distinguish them with few annotated instances due to high co-occurrence of some relations . |
| Approach: | They propose a few-shot relation classification model with two mechanisms to decouple easily-confused relations. |
| Outcome: | The proposed model achieves comparable and even better results to strong baselines in terms of accuracy. |
RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis (2025.findings-acl)
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Pengzuo Wu, Yuhang Yang, Guangcheng Zhu, Chao Ye, Hong Gu, Xu Lu, Ruixuan Xiao, Bowen Bao, Yijing He, Liangyu Zha, Wentao Ye, Junbo Zhao, Haobo Wang
| Challenge: | Existing benchmarks for large language models focus on simple, flat table structures. |
| Approach: | They propose a benchmark to evaluate the performance of both Large Language Models and Multimodal LLMs across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. |
| Outcome: | The proposed benchmark evaluates the performance of LLMs and Multimodal LLM models across a variety of input formats for complex tabular data, including LaTeX, HTML, and PNG. |
Set the Clock: Temporal Alignment of Pretrained Language Models (2024.findings-acl)
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| Challenge: | Language models (LMs) are trained on web text originating from many points in time and, in general, without any explicit temporal grounding. |
| Approach: | They construct a time-sensitive question dataset and use it to examine temporal alignment methods to align their internal knowledge to a target time. |
| Outcome: | The proposed methods improve LLaMa2's performance by 62% if they are fine tuned to the year 2022 . |
Causal Document-Grounded Dialogue Pre-training (2023.emnlp-main)
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Yingxiu Zhao, Bowen Yu, Bowen Li, Haiyang Yu, Jinyang Li, Chao Wang, Fei Huang, Yongbin Li, Nevin Zhang
| Challenge: | Existing methods for document-grounded dialogue (DocGD) rely on general pre-trained language models without a tailored pre-training approach that explicitly captures causal relationships. |
| Approach: | They propose a causally-complete dataset construction strategy for developing million-scale DocGD pre-training corpora and a perturbation-based strategy to capture causality. |
| Outcome: | The proposed strategy yields significant and consistent improvements in fully-supervised, low-resource, few-shot, and zero-shot settings. |
Bridging Reasoning and Action: Hybrid LLM–RL Framework for Efficient Cross-Domain Task-Oriented Dialogue (2026.findings-acl)
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| Challenge: | Existing methods to solve cross-domain task-oriented dialogues are brittle when cross- domain constraints are not directly grounded in surface text or require commonsense inference. |
| Approach: | They propose a framework that makes LLM-derived constraint reasoning usable for RL. |
| Outcome: | Experiments show that the proposed framework outperforms single-model baselines on long-horizon tasks. |
RoBGuard: Enhancing LLMs to Assess Risk of Bias in Clinical Trial Documents (2025.coling-main)
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Changkai Ji, Bowen Zhao, Zhuoyao Wang, Yingwen Wang, Yuejie Zhang, Ying Cheng, Rui Feng, Xiaobo Zhang
| Challenge: | Existing approaches to assess the risk of bias in RCTs focus on manually crafted prompts and a restricted set of simple questions, limiting their accuracy and generalizability. |
| Approach: | They propose a framework for enhancing Large Language Models to assess the risk of bias in RCTs by reformulation, document parsing and multi-expert collaboration. |
| Outcome: | The proposed framework outperforms existing methods on the RoB-Item and RoB domains. |
DIVE into MoE: Diversity-Enhanced Reconstruction of Large Language Models from Dense into Mixture-of-Experts (2025.acl-long)
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| Challenge: | Existing methods for reconstruction of large language models overlook diversity among experts, leading to potential redundancy. |
| Approach: | They propose a pruning-based expert reconstruction method that prunes a specific LLM and retrains it on routers, experts and normalization modules. |
| Outcome: | The proposed method outperforms pruning and MoE reconstruction methods on Llama-style models with open-source training corpora. |
R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning (2026.findings-acl)
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Qingfei Zhao, Ruobing Wang, Dingling Xu, Daren Zha, Ma Bowen, Zhichun Wang, Shijie Jia, Limin Liu, Xin Wang
| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities in multi-step and long-chain reasoning, but extending their reasoning capabilities to encompass deep interactions with search remains a non-trivial challenge. |
| Approach: | They propose a framework for Reasoning–Search integration that integrates multi-reward signals to optimize the reasoning–search interaction trajectories. |
| Outcome: | Experiments on seven datasets show that R-Search significantly outperforms mainstream RAG baselines. |
RoR: Read-over-Read for Long Document Machine Reading Comprehension (2021.findings-emnlp)
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| Challenge: | Existing models for machine reading comprehension are limited to individual chunks due to encoding length constraint. |
| Approach: | They propose a read-over-read method that expands the reading field from chunk to document by predicting regional answers for each chunk. |
| Outcome: | Extensive experiments on QuAC and TriviaQA show that the proposed model performs well for long document reading. |
Mirror in the Model: Ad Banner Image Generation via Reflective Multi-LLM and Multi-modal Agents (2025.emnlp-industry)
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| Challenge: | Recent advances in generative modeling have greatly improved image synthesis quality. |
| Approach: | They propose an agentic refinement framework for automatic ad banner generation that integrates a hierarchical multimodal agent system with a coordination loop. |
| Outcome: | The proposed model outperforms existing models in real-world banner design scenarios. |
LLMBox: A Comprehensive Library for Large Language Models (2024.acl-demos)
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Tianyi Tang, Hu Yiwen, Bingqian Li, Wenyang Luo, ZiJing Qin, Haoxiang Sun, Jiapeng Wang, Shiyi Xu, Xiaoxue Cheng, Geyang Guo, Han Peng, Bowen Zheng, Yiru Tang, Yingqian Min, Yushuo Chen, Jie Chen, Ranchi Zhao, Luran Ding, Yuhao Wang, Zican Dong, Xia Chunxuan, Junyi Li, Kun Zhou, Xin Zhao, Ji-Rong Wen
| Challenge: | a library to facilitate the development, use, and evaluation of large language models (LLMs) is presented. |
| Approach: | They propose a unified library to facilitate the development, use and evaluation of large language models (LLMs). |
| Outcome: | The proposed library is based on extensive experiments in a variety of evaluation settings. |
AdaptFlow: Adaptive Workflow Optimization via Meta-Learning (2025.findings-emnlp)
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Runchuan Zhu, Bowen Jiang, Lingrui Mei, Fangkai Yang, Lu Wang, Haoxiang Gao, Fengshuo Bai, Pu Zhao, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang
| Challenge: | Existing approaches to large language models rely on static templates or manual workflows. |
| Approach: | AdaptFlow is a language-based meta-learning framework inspired by model-agnostic meta- learning. |
| Outcome: | AdaptFlow outperforms manual and automated workflows on question answering, code generation and mathematical reasoning benchmarks. |
API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs (2023.emnlp-main)
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Minghao Li, Yingxiu Zhao, Bowen Yu, Feifan Song, Hangyu Li, Haiyang Yu, Zhoujun Li, Fei Huang, Yongbin Li
| Challenge: | Recent research has demonstrated that Large Language Models (LLMs) can enhance their capabilities by utilizing external tools. |
| Approach: | They propose a runnable evaluation system consisting of 73 API tools and an annotation system for 314 tool-use dialogues with 753 API calls. |
| Outcome: | The proposed benchmark assesses the effectiveness of existing LLMs by analyzing 314 tool-use dialogues with 753 API calls. |
Stance Detection on Social Media with Background Knowledge (2023.emnlp-main)
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| Challenge: | Existing studies of stance detection focus on learning stance information about specific targets from context, but in real-world scenarios, we usually have a certain understanding of a target when we express our stance on it. |
| Approach: | They propose to take the background knowledge of the target into account for better stance detection by categorizing it into episodic and discourse knowledge categories and a heuristic retrieval algorithm based on the topic to retrieve the Wikipedia documents relevant to the sample. |
| Outcome: | The proposed framework achieves state-of-the-art on four benchmark datasets showing that the proposed framework is able to detect stances in-target and zero-shot scenarios. |
Large Language Models are Complex Table Parsers (2023.emnlp-main)
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| Challenge: | Extensive experiments and results on Complex Table QA datasets, i.e., the open-domain dataset HiTAB and the aviation domain dataset AIT-QA show that our approach significantly outperforms previous work on both datasets. |
| Approach: | They propose to incorporate Generative Pre-trained Transformer 3.5 to address the specific challenges posed by Complex Table QA by reconstructing tables into tuples and using prompt templates to create dialogues. |
| Outcome: | The proposed approach outperforms previous work on complex table parsing datasets and leads to state-of-the-art (SOTA) performance. |
Tree-Instruct: A Preliminary Study of the Intrinsic Relationship between Complexity and Alignment (2024.lrec-main)
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| Challenge: | Extensive research has highlighted the importance of data complexity as a crucial metric, but the impact of complexity remains relatively unexplored. |
| Approach: | They propose to add a specified number of nodes to instructions’ semantic trees to enhance the instruction complexity in a controllable manner. |
| Outcome: | The proposed approach outperforms diverse yet complex instructions under the same token budget and can control the difficulty level of modified instructions. |