Papers by Can Xu

ADAM: Dense Retrieval Distillation with Adaptive Dark Examples (2024.findings-acl)

Challenge:	Currently, retrieval-based dialogues are performed in shallow ways . a recent study investigated the problem of context-response matching in open-domain .
Approach:	They propose a model that lets utterance-response interaction go deep by stacking interaction blocks.
Outcome:	The proposed model outperforms state-of-the-art methods on three benchmark data sets.

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Chongyang Tao, Chang Liu, Tao Shen, Can Xu, Xiubo Geng, Binxing Jiao, Daxin Jiang

RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models (2022.aacl-main)

Challenge:	Existing methods to retrieve data from multiple encoders are too trivial for the teacher to distinguish, preventing the teacher from transferring abundant dark knowledge to the student.
Approach:	They propose a knowledge distillation framework that can better transfer the dark knowledge held in the teacher with adaptive dark examples.
Outcome:	The proposed framework can better transfer the dark knowledge held in the teacher with adaptive dark examples.

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Lingzhi Wang, Huang Hu, Lei Sha, Can Xu, Daxin Jiang, Kam-Fai Wong

Adversarial Yet Cooperative: Multi-Perspective Reasoning in Retrieved-Augmented Language Models (2026.findings-acl)

Challenge:	Existing generative methods to recommend items are shallowly integrated into the model training and have poor chit-chat ability.
Approach:	They propose a framework that integrates recommendation into the dialog generation by introducing a vocabulary pointer.
Outcome:	The proposed framework outperforms the state-of-the-art models on a benchmark dataset.

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Can Xu, Lingyong Yan, Jiayi Wu, Haosen Wang, Shuaiqiang Wang, Yuchen Li, Jizhou Huang, Dawei Yin, Xiang Li

Responsible & Trustworthy NLP

Challenge:	Existing training paradigms rely on outcome-oriented rewards, which provide insufficient signal for shaping the complex, multi-step reasoning process.
Approach:	They propose a framework that integrates large reasoning models with retrieval-augmented generation to improve reasoning fidelity and verification rigor.
Outcome:	Experiments on multiple benchmarks demonstrate the effectiveness of the proposed framework.

Contextual Fine-to-Coarse Distillation for Coarse-grained Response Selection in Open-Domain Conversations (2022.acl-long)

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Wei Chen, Yeyun Gong, Can Xu, Huang Hu, Bolun Yao, Zhongyu Wei, Zhihao Fan, Xiaowu Hu, Bartuer Zhou, Biao Cheng, Daxin Jiang, Nan Duan

MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation (2023.acl-long)

Challenge:	Existing studies focus on coarse-grained response selection in retrieval-based dialogue systems.
Approach:	They propose a Contextual Fine-to-Coarse (CFC) distilled model for coarse-grained response selection in open-domain conversations.
Outcome:	The proposed model improves over baseline methods on two datasets based on the Reddit comments dump and Twitter corpus compared with baseline methods.

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Jiazhan Feng, Qingfeng Sun, Can Xu, Pu Zhao, Yaming Yang, Chongyang Tao, Dongyan Zhao, Qingwei Lin

PCL: Peer-Contrastive Learning with Diverse Augmentations for Unsupervised Sentence Embeddings (2022.emnlp-main)

Challenge:	MMDialog is a dataset of 1.08 million real-world dialogues with 1.53 million unique images across 4,184 topics.
Approach:	They propose to use a curated set of 1.08 million dialogues with 1.53 million unique images to generalize the open domain.
Outcome:	The proposed system can predict responses to multi-modal content with state-of-the-art techniques and measure their performance.

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Qiyu Wu, Chongyang Tao, Tao Shen, Can Xu, Xiubo Geng, Daxin Jiang

Read, Attend and Comment: A Deep Architecture for Automatic News Comment Generation (D19-1)

Challenge:	Existing approaches to learning sentence embeddings in unsupervised manner depend on mono-augmenting . existing approaches depend on augmenting biases and thus corrupt the quality of sentence embeds.
Approach:	They propose a method to augment a sentence with a semantically-close positive instance to construct contrastive pairs in unsupervised manner.
Outcome:	The proposed method improves performance on STS benchmarks and compares with existing methods.

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Ze Yang, Can Xu, Wei Wu, Zhoujun Li

Neural Response Generation with Meta-words (P19-1)

Challenge:	Existing methods for news comment generation have not been well studied.
Approach:	They propose a “read-attend-comment” procedure for automatic news comment generation and formalize it with a reading network and a generation network.
Outcome:	The proposed procedure outperforms existing methods in terms of automatic evaluation and human judgment on two public datasets.

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Can Xu, Wei Wu, Chongyang Tao, Huang Hu, Matt Schuerman, Ying Wang

Synergistic Interplay between Search and Large Language Models for Information Retrieval (2024.acl-long)

Challenge:	Experimental results show that meta-words can be used to generate open domain dialogues . human-machine conversation is a fundamental problem in NLP .
Approach:	They propose a goal-tracking memory network that formalizes meta-word expression as a target in response generation and manages the generation process with a state memory panel and a controller.
Outcome:	The proposed model outperforms state-of-the-art generation models in response relevance, response diversity, and accuracy.

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Jiazhan Feng, Chongyang Tao, Xiubo Geng, Tao Shen, Can Xu, Guodong Long, Dongyan Zhao, Daxin Jiang

WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models (2025.acl-long)

Challenge:	Information retrieval (IR) is an indispensable technique for locating relevant resources from vast amounts of data.
Approach:	They propose a framework that facilitates information refinement through synergy between RMs and LLMs.
Outcome:	The proposed framework improves the performance of large-scale retrieval benchmarks on web searches and low-resource retrieval tasks.

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Huawen Feng, Pu Zhao, Qingfeng Sun, Can Xu, Fangkai Yang, Lu Wang, Qianli Ma, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang

LONGAGENT: Achieving Question Answering for 128k-Token-Long Documents through Multi-Agent Collaboration (2024.emnlp-main)

Challenge:	Recent code large language models have demonstrated impressive performance on code-related tasks.
Approach:	They propose a paradigm that learns from expert battles to address these limitations . they create an arena where leading LLMs challenge each other with evaluations .
Outcome:	The proposed model improves on existing models by leveraging expert battles . it achieves state-of-the-art performance even without relying on proprietary models .

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Jun Zhao, Can Zu, Xu Hao, Yi Lu, Wei He, Yiwen Ding, Tao Gui, Qi Zhang, Xuanjing Huang

Question Answering

Challenge:	Large language models (LLMs) have been successful in understanding language and processing text, but their cost prohibits their practical applications.
Approach:	They propose a multi-agent collaboration method that breaks down lengthy documents into smaller, more manageable chunks and organizes the member agents to read their assigned chunks.
Outcome:	The proposed method achieves 16.42% and 1.63% accuracy gains over existing models on single-hop and multi-hop QA settings.

Knowledge-Grounded Dialogue Generation with Pre-trained Language Models (2020.emnlp-main)

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Xueliang Zhao, Wei Wu, Can Xu, Chongyang Tao, Dongyan Zhao, Rui Yan

Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic Knowledge Graph (2025.findings-acl)

Challenge:	Empirical results indicate that pre-trained language models can significantly outperform state-of-the-art methods in both automatic evaluation and human judgment.
Approach:	They propose to equip a pre-trained language model with a knowledge selection module to generate knowledge-grounded dialogues.
Outcome:	The proposed model outperforms state-of-the-art methods in evaluation and human judgment.

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Yibo Zhao, Jiapeng Zhu, Can Xu, Yao Liu, Xiang Li

Multimodal Dialogue Response Generation (2022.acl-long)

Challenge:	Existing methods to address toxicity issues with large language models are inadequate . lack of domain-specific knowledge leads to false negatives and excessive sensitivity to toxic speech limits freedom of speech.
Approach:	They propose a method that leverages graph search on a meta-toxic knowledge graph to enhance hatred and toxicity detection.
Outcome:	The proposed method lowers false positive rate and improves toxicity detection performance in out-of-domain scenarios.

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Qingfeng Sun, Yujing Wang, Can Xu, Kai Zheng, Yaming Yang, Huang Hu, Fei Xu, Jessica Zhang, Xiubo Geng, Daxin Jiang

ProphetNet-X: Large-Scale Pre-training Models for English, Chinese, Multi-lingual, Dialog, and Code Generation (2021.acl-demo)

Challenge:	Existing studies focus on multimodal dialogue models but neglect generation methods.
Approach:	They propose a multimodal dialogue response generation task which requires multimodal dialogs containing both texts and images which are difficult to obtain.
Outcome:	Experiments show that the proposed model can generate informative text and high-resolution image responses.

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Weizhen Qi, Yeyun Gong, Yu Yan, Can Xu, Bolun Yao, Bartuer Zhou, Biao Cheng, Daxin Jiang, Jiusheng Chen, Ruofei Zhang, Houqiang Li, Nan Duan

Leveraging Large Language Models for NLG Evaluation: Advances and Challenges (2024.emnlp-main)

Challenge:	Existing models for pre-training are not convenient for users to find and set them up.
Approach:	They propose to extend ProphetNet into other domains and languages by pre-training models . they pre-train a cross-lingual generation model ProphetNet-Multi and a Chinese generation model .
Outcome:	The proposed models achieve new state-of-the-art on 10 benchmarks.

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Zhen Li, Xiaohan Xu, Tao Shen, Can Xu, Jia-Chen Gu, Yuxuan Lai, Chongyang Tao, Shuai Ma

Learning Autonomous Driving Tasks via Human Feedbacks with Large Language Models (2024.findings-emnlp)

Challenge:	introducing Large Language Models (LLMs) has opened new avenues for assessing generated content quality, e.g., coherence, creativity, and context relevance.
Approach:	They propose a taxonomy for organizing existing LLM-based evaluation metrics and a structured framework to understand and compare them.
Outcome:	The proposed taxonomy offers a framework to understand and compare LLM-based evaluation methods.

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Yunsheng Ma, Xu Cao, Wenqian Ye, Can Cui, Kai Mei, Ziran Wang

Re-Reading Improves Reasoning in Large Language Models (2024.emnlp-main)

Challenge:	Existing systems focus on making autonomous driving decisions without human interaction, but human-like decision-making is still an important factor in designing autonomous driving systems.
Approach:	They propose a framework leveraging Large Language Models for learning human-centered driving decisions from diverse simulation scenarios and environments that incorporate human feedback.
Outcome:	The proposed framework can match baseline extensively trained reinforcement learning models in driving scenarios and store optimal driving programming policy using Retrieval Augmented Generation (RAG).

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Xiaohan Xu, Chongyang Tao, Tao Shen, Can Xu, Hongbo Xu, Guodong Long, Jian-Guang Lou, Shuai Ma

PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks (2022.acl-long)

Challenge:	Unlike thought-eliciting prompting methods, RE2 shifts the focus to the input by processing questions twice, thereby enhancing the understanding process.
Approach:	They introduce a simple, yet general and effective prompting method, RE2, which rereads the question as input.
Outcome:	The proposed method demonstrates strong generality and compatibility with most thought-eliciting prompting methods, including CoT.

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Yufei Wang, Can Xu, Qingfeng Sun, Huang Hu, Chongyang Tao, Xiubo Geng, Daxin Jiang

Low-Resource NLP

Challenge:	Existing approaches to build labeled training data from domain-specific data are expensive to obtain.
Approach:	They propose a Prompt-based Data Augmentation model which only trains small-scale Soft Promptes in frozen Pre-trained Language Models.
Outcome:	The proposed model outperforms several baseline models on four benchmarks and is complementary with unlabeled in-domain data.

GCIG: GraphRAG-based Cross-document Instruction Generation for Boosting LLM Reasoning (2026.findings-acl)

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Xiaoliang Xu, Huang Yuan, Junmei Wang, Can Xu

Learning to Ground Visual Objects for Visual Dialog (2021.findings-emnlp)

Challenge:	Existing methods for fine-tuning large language models struggle in knowledge-intensive domains and complex reasoning tasks due to their limited coverage of single-document knowledge and repetitive content.
Approach:	They propose a GraphRAG-based cross-document instruction generation framework that generates diverse questions through task-aware prompts and context-sensitive retrieval.
Outcome:	The proposed framework outperforms existing methods on knowledge-intensive and multi-hop question-answering tasks.

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Feilong Chen, Xiuyi Chen, Can Xu, Daxin Jiang

Low-Resource Response Generation with Template Prior (D19-1)

Challenge:	Existing methods to ground visual objects are inadequate for visual dialog . a posterior distribution is inferred from context and questions, while posterior distributions are used to facilitate visual objects grounding.
Approach:	They propose a method to learn to ground visual objects for visual dialog using prior and posterior distributions over visual objects to facilitate visual objects grounding.
Outcome:	The proposed approach improves the existing models in generative and discriminative settings by a significant margin.

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Ze Yang, Wei Wu, Jian Yang, Can Xu, Zhoujun Li

TegTok: Augmenting Text Generation via Task-specific and Open-world Knowledge (2022.findings-acl)

Challenge:	Existing open domain response generation models are limited to paired data, but are less explored in real-world applications.
Approach:	They propose to train a neural response generation model with unpaired data and paired data as prior.
Outcome:	The proposed model outperforms state-of-the-art models in both automatic and human evaluation when only a few pairs are available.

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Chao-Hong Tan, Jia-Chen Gu, Chongyang Tao, Zhen-Hua Ling, Can Xu, Huang Hu, Xiubo Geng, Daxin Jiang

Playing 20 Question Game with Policy-Based Reinforcement Learning (D18-1)

Challenge:	Generating natural and informative texts has been a long-standing problem in NLP.
Approach:	They propose to augment TExt Generation via Task-specific and Open-world Knowledge in a unified framework.
Outcome:	The proposed model can learn what and how to generate on two text generation tasks.

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Huang Hu, Xianchao Wu, Bingfeng Luo, Chongyang Tao, Can Xu, Wei Wu, Zhan Chen

Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models (2026.findings-acl)

Challenge:	The 20 Questions (Q20) game encourages deductive reasoning and creativity.
Approach:	They propose a policy-based Reinforcement Learning method which learns optimal question selection . the method is robust to noisy answers and uses a reward network to estimate the more informative reward .
Outcome:	The proposed method outperforms an entropy-based engineering system and has competitive performance in noisy-free simulation environment.

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Qiyuan Zhang, Yufei Wang, Tianhe Wu, Can Xu, Qingfeng Sun, Kai Zheng, Xue Liu, Chen Ma

Reasoning

Challenge:	Recent advances in Generative Reward Models have demonstrated that scaling the length of Chain-of-Thought reasoning enhances reliability of evaluation.
Approach:	They propose a framework that reconfigures raw rationales into structured Breadth-CoT and Depth-Co T through a modular synthesis pipeline.
Outcome:	The proposed framework surpasses open-source RMs by an average of 8.2%.

Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks (2020.emnlp-main)

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Yufan Zhao, Can Xu, Wei Wu

Maria: A Visual Experience Powered Conversational Agent (2021.acl-long)

Challenge:	Existing approaches to multi-turn response generation for open-domain dialogues have a complexity problem . auxiliary tasks that relate to context understanding can guide the learning of the generation model .
Approach:	They propose a multi-turn response generation model that has a simple structure yet can effectively leverage conversation contexts for response generation.
Outcome:	The proposed model outperforms state-of-the-art models in response quality and human judgment . it also enjoys a faster decoding process .

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Zujie Liang, Huang Hu, Can Xu, Chongyang Tao, Xiubo Geng, Yining Chen, Fan Liang, Daxin Jiang

Automatic Instruction Evolving for Large Language Models (2024.emnlp-main)

Challenge:	Existing studies focus on grounding conversational agents on text-only corpora, but they lack the perception ability to our physical world.
Approach:	They propose to ground conversational agents on images retrieved from large-scale image indexes . they propose to use visual knowledge to generate informative responses based on the extracted knowledge .
Outcome:	The proposed agent outperforms state-of-the-art methods on automatic metrics and human evaluation.

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Weihao Zeng, Can Xu, Yingxiu Zhao, Jian-Guang Lou, Weizhu Chen

MPC-BERT: A Pre-Trained Language Model for Multi-Party Conversation Understanding (2021.acl-long)

Challenge:	Evol-Instruct is an end-to-end framework that evolves instruction datasets without human effort.
Approach:	They propose an end-to-end framework that evolves instruction datasets without human effort by analyzing and analyzing evolutionary strategies for the given instruction data.
Outcome:	The proposed method outperforms human-designed methods on various benchmarks including MT-Bench, AlpacaEval, GSM8K, and HumanEval.

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Jia-Chen Gu, Chongyang Tao, Zhenhua Ling, Can Xu, Xiubo Geng, Daxin Jiang

Towards Robust Ranker for Text Retrieval (2023.findings-acl)

Challenge:	Existing models for multi-party conversation represent interlocutors and utterances individually . existing methods ignore complicated structure of MPC which may provide crucial interlocutor and tertiary semantics.
Approach:	They propose a pre-trained model for multi-party conversation that considers learning who says what to whom in a unified model with elaborated self-supervised tasks.
Outcome:	The proposed model outperforms existing models on three downstream tasks at two benchmarks.

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Yucheng Zhou, Tao Shen, Xiubo Geng, Chongyang Tao, Can Xu, Guodong Long, Binxing Jiao, Daxin Jiang

WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning (2024.acl-long)

Challenge:	Existing methods for text retrieval are based on a 'retrieval & rerank' pipeline, which uses a fast retriever to fetch a set of top document candidates, while a robust ranker is based upon a weak negative mining during contrastive learning.
Approach:	They propose a multi-adversarial training strategy that leverages multiple retrievers as generators to challenge a ranker.
Outcome:	The proposed model outperforms the existing de facto ranker training paradigms on the passage retrieval benchmarks using BM25-reranking, full-ranking and retriever distillation.

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Zhaojian Yu, Xin Zhang, Ning Shang, Yangyu Huang, Can Xu, Yishujie Zhao, Wenxiang Hu, Qiufeng Yin

StyleDGPT: Stylized Response Generation with Pre-trained Language Models (2020.findings-emnlp)

Challenge:	Recent work shows that Code Large Language Models can address a wide range of code-related tasks.
Approach:	They propose a method to generate widespread and versatile instruction data from open source code datasets and use it to train code-related models.
Outcome:	The proposed model outperforms open-source models in generalization ability across code-related tasks.

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Ze Yang, Wei Wu, Can Xu, Xinnian Liang, Jiaqi Bai, Liran Wang, Wei Wang, Zhoujun Li

Learning Neural Templates for Recommender Dialogue System (2021.emnlp-main)

Challenge:	Existing methods for generating responses following a desired style are lacking of parallel data for training.
Approach:	They propose a KL loss and a style classifier to fine-tune response generation . they show that their model can significantly outperform state-of-the-art methods .
Outcome:	The proposed model outperforms state-of-the-art models in style consistency and contextual coherence with two public datasets.

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Zujie Liang, Huang Hu, Can Xu, Jian Miao, Yingying He, Yining Chen, Xiubo Geng, Fan Liang, Daxin Jiang