Papers by Mao Zheng
Cross-Lingual Phrase Retrieval (2022.acl-long)
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| Challenge: | Existing approaches to cross-lingual phrase retrieval learn word or sentence representations in word or sentences. |
| Approach: | They propose a cross-lingual phrase retrieval model that extracts phrase representations from unlabeled example sentences. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a large-scale cross-lingual phrase retrieval dataset, showing it can perform in an unseen language pair during training. |
RAG-Studio: Towards In-Domain Adaptation of Retrieval Augmented Generation Through Self-Alignment (2024.findings-emnlp)
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| Challenge: | Existing RAG systems that use pre-trained LLMs and retrievers often fail in specialized domains and applications. |
| Approach: | They propose a self-aligned training framework that adapts general RAG models to specific domains solely through synthetic data. |
| Outcome: | Experiments on specialized domain corpus, general LLM, and general retriever show that the self-aligned training framework outperforms human-annotated training data in specialized fields. |
XLM-E: Cross-lingual Language Model Pre-training via ELECTRA (2022.acl-long)
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Zewen Chi, Shaohan Huang, Li Dong, Shuming Ma, Bo Zheng, Saksham Singhal, Payal Bajaj, Xia Song, Xian-Ling Mao, Heyan Huang, Furu Wei
| Challenge: | ELECTRA-style tasks are used to pretrain cross-lingual models for NLP tasks . masked language modeling tasks require massive computation resources, rendering such models quite expensive . |
| Approach: | They propose to use ELECTRA-style tasks to pre-train a cross-lingual language model . they propose to pretrain the model on multilingual and parallel corpora . |
| Outcome: | The proposed model outperforms baseline models on cross-lingual understanding tasks with much less computation cost. |
Can We Predict Before Executing Machine Learning Agents? (2026.acl-long)
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| Challenge: | Existing approaches to scientific discovery rely on expensive physical execution . a Generate-Execute-Feedback paradigm is costly and slow . |
| Approach: | They propose to internalize execution priors to substitute costly runtime checks with instantaneous predictive reasoning, drawing inspiration from World Models. |
| Outcome: | The proposed framework achieves 61.5% accuracy and robust confidence calibration when primed with a Verified Data Analysis Report. |
Can Many-Shot In-Context Learning Help LLMs as Evaluators? A Preliminary Empirical Study (2025.coling-main)
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| Challenge: | Existing evaluation approaches to evaluate Large Language Models are affected by potential biases within LLMs. |
| Approach: | They propose two many-shot In-Context Learning (ICL) prompt templates to help LLM evaluators mitigate potential biases. |
| Outcome: | The proposed templates reduce biases by using in-context examples with model-generated rationales as references. |
ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Training (2024.acl-long)
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Le Zhuo, Zewen Chi, Minghao Xu, Heyan Huang, Jianan Zhao, Heqi Zheng, Conghui He, Xian-Ling Mao, Wentao Zhang
| Challenge: | Experimental results demonstrate that ProtLLM achieves superior performance against protein-specialized baselines on protein-centric tasks and induces zero-shot and in-context learning capabilities on protein language tasks. |
| Approach: | They propose a cross-modal large language model (LLM) that can handle protein-centric and protein-language tasks by using a dynamic protein mounting mechanism. |
| Outcome: | The proposed model can predict proteins from a vast pool of candidates and can also predict natural language and biological papers. |
FastCuRL: Curriculum Reinforcement Learning with Stage-wise Context Scaling for Efficient Training R1-like Reasoning Models (2025.findings-emnlp)
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| Challenge: | Improving training efficiency remains a challenge in large-scale Reinforcement Learning (RL). |
| Approach: | They propose a curriculum RL framework with stage-wise context scaling to improve RL training efficiency. |
| Outcome: | The proposed framework outperforms state-of-the-art reasoning models on five benchmarks and achieves 49.6% accuracy on AIME 2024. |
UniConv: Unifying Retrieval and Response Generation for Large Language Models in Conversations (2025.acl-long)
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Fengran Mo, Yifan Gao, Chuan Meng, Xin Liu, Zhuofeng Wu, Kelong Mao, Zhengyang Wang, Pei Chen, Zheng Li, Xian Li, Bing Yin, Meng Jiang
| Challenge: | Existing conversational search systems are usually built with two different models . this separation restricts the system from leveraging the model's intrinsic knowledge simultaneously . Existing studies for developing unified models cannot fully address the aspects of understanding conversational context, managing retrieval independently, and generating responses. |
| Approach: | They propose to unify dense retrieval and response generation for large language models in conversation by fine-tuning and mitigating data discrepancy. |
| Outcome: | The proposed model can outperform existing models on five conversational search datasets and reduce inconsistency risks while mitigating data discrepancy. |
Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data (2020.emnlp-main)
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| Challenge: | Existing research has focused on training open-domain dialogue models using unpaired data. |
| Approach: | They propose a data-level distillation method for training open-domain dialogue models by utilizing unpaired data. |
| Outcome: | The proposed method produces high-quality dialogue pairs with diverse contents, and can improve competitive baselines. |
DualRAG: A Dual-Process Approach to Integrate Reasoning and Retrieval for Multi-Hop Question Answering (2025.acl-long)
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Rong Cheng, Jinyi Liu, Yan Zheng, Fei Ni, Jiazhen Du, Hangyu Mao, Fuzheng Zhang, Bo Wang, Jianye Hao
| Challenge: | Existing approaches to multi-hop question answering struggle to identify and organize dynamic knowledge . et al., 2023; Liu e.t. al. 2023) suggest a dual-process framework for multi-step reasoning . |
| Approach: | They propose a synergistic dual-process framework that integrates reasoning and retrieval. |
| Outcome: | The proposed framework improves answer accuracy and coherence even in smaller-scale models. |
ChatRetriever: Adapting Large Language Models for Generalized and Robust Conversational Dense Retrieval (2024.emnlp-main)
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| Challenge: | a conversational search system requires accurate interpretation of user intent from complex multi-turn contexts. |
| Approach: | They propose a dual-learning approach that adapts LLMs for retrieval via contrastive learning while enhancing the complex session understanding through masked instruction tuning. |
| Outcome: | The proposed approach outperforms existing retrieval methods on five conversational search benchmarks. |
Transferable Persona-Grounded Dialogues via Grounded Minimal Edits (2021.emnlp-main)
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| Challenge: | Existing grounded dialogue models are limited by the distribution of data and the type of grounded concepts. |
| Approach: | They propose a framework which edits existing responses to be grounded on a given concept by disentangling and recombining persona-related and persona agnostic parts of the response. |
| Outcome: | The proposed framework outperforms baselines on the personaMi-nEdit dataset and shows that it can improve persona consistency while preserving the use of knowledge and empathy. |
SciMRC: Multi-perspective Scientific Machine Reading Comprehension (2024.lrec-main)
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| Challenge: | Existing datasets focused on single-perspective question-answer pairs overlooking inherent variation in comprehension levels among different readers. |
| Approach: | They propose a multi-perspective scientific machine reading comprehension dataset . their dataset comprises 741 scientific papers and 6,057 question-answer pairs . |
| Outcome: | The proposed dataset includes questions from beginners, students, and experts. |
Grounding Language Model with Chunking-Free In-Context Retrieval (2024.acl-long)
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| Challenge: | CFIC retrieval approach eliminates the need for document chunking and provides a more efficient and efficient method for RAG systems. |
| Approach: | They propose a Chunking-Free In-Context retrieval approach specifically tailored for RAG systems . they employ auto-aggressive decoding to accurately identify specific evidence text . |
| Outcome: | The proposed method is better than traditional methods on open question answering datasets. |
HyperCRS: Hypergraph-Aware Multi-Grained Preference Learning to Burst Filter Bubbles in Conversational Recommendation System (2025.findings-acl)
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Yongsen Zheng, Mingjie Qian, Guohua Wang, Yang Liu, Ziliang Chen, Mingzhi Mao, Liang Lin, Kwok-Yan Lam
| Challenge: | Existing methods to analyze filter bubbles in the static recommendation environment are unable to burst them during user interactions. |
| Approach: | They propose a paradigm to learn multi-grained user preferences during dynamic user-system interactions via natural language conversations to burst filter bubbles. |
| Outcome: | The proposed paradigm achieves state-of-the-art performance and the superior of bursting filter bubbles in the conversational recommendation system. |
Counting-Stars: A Multi-evidence, Position-aware, and Scalable Benchmark for Evaluating Long-Context Large Language Models (2025.coling-main)
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| Challenge: | Existing benchmarks for long-context language models have lagged behind . however, there is still room for improvement as the context window and complexity of the tasks increase. |
| Approach: | They propose a long-context benchmark to evaluate the performance of long-text language models. |
| Outcome: | The proposed benchmarks show that the models perform better in long-context environments as the context window increases and complexity increases. |
EasyEdit: An Easy-to-use Knowledge Editing Framework for Large Language Models (2024.acl-demos)
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Peng Wang, Ningyu Zhang, Bozhong Tian, Zekun Xi, Yunzhi Yao, Ziwen Xu, Mengru Wang, Shengyu Mao, Xiaohan Wang, Siyuan Cheng, Kangwei Liu, Yuansheng Ni, Guozhou Zheng, Huajun Chen
| Challenge: | Large Language Models (LLMs) suffer from knowledge cutoff or fallacy issues, which means they are unaware of unseen events or generate text with incorrect facts owing to outdated/noisy data. |
| Approach: | They propose an easy-to-use knowledge editing framework for Large Language Models that allows users to easily edit updated knowledge and adjust undesired behavior while minimizing the impact on unrelated inputs. |
| Outcome: | The proposed framework surpasses traditional fine-tuning in terms of reliability and generalization. |
MiMoTable: A Multi-scale Spreadsheet Benchmark with Meta Operations for Table Reasoning (2025.coling-main)
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| Challenge: | Existing benchmarks for table reasoning are incomplete due to the complexity of the tables and user questions in real-world applications. |
| Approach: | They propose a Multi-scale spreadsheet benchmark with Meta operations for Table reasoning that incorporates two key features and a new criterion with six categories of meta operations for measuring the difficulty of each question. |
| Outcome: | The proposed model outperforms Claude-3.5-Sonnet with 77.4% accuracy on the existing benchmarks. |
PodBench: A Comprehensive Benchmark for Instruction-Aware Audio-Oriented Podcast Script Generation (2026.acl-long)
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| Challenge: | Podcast script generation is a challenging task for large language models, but evaluation resources are limited. |
| Approach: | They propose a benchmark to evaluate podcast script generation using a multifaceted evaluation framework . PodBench is a prototype that integrates quantitative constraints with LLM-based quality assessment . |
| Outcome: | The proposed framework integrates quantitative constraints with LLM-based quality assessment. |
Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment (2021.acl-long)
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| Challenge: | Experimental results show that denoising word alignment improves cross-lingual transferability . most applications and resources are still English-centric, making non-English users hard to access. |
| Approach: | They propose to denoise word alignment as a cross-lingual pre-training task . they first self-label word alignments for parallel sentences and then mask tokens . |
| Outcome: | The proposed model improves cross-lingual transferability on token-level tasks, especially on question answering, and structured prediction. |
SSR-Zero: Simple Self-Rewarding Reinforcement Learning for Machine Translation (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities in machine translation, but most MT-specific LLMs rely heavily on external supervision during training. |
| Approach: | They propose a reinforcement learning framework for machine translation that is reference-free and relies solely on self-judging rewards. |
| Outcome: | The proposed framework outperforms existing LLMs and larger general LLM models on English Chinese translation benchmarks and performs competitively with leading closed-source systems. |
DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining (2023.emnlp-main)
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| Challenge: | Existing text mining models are fine-tuned by fine-timing a large pre-trained language model (PLM) in downstream tasks. |
| Approach: | They propose a semi-supervised learning framework for fine-tuning a cohort of small student models generated from a large pre-trained language model using knowledge distillation. |
| Outcome: | The proposed framework outperforms baseline models on semi-supervised text classification and extractive summarization tasks while maintaining comparable performance. |