Papers by Quan Liu

Cool-Fusion: Fuse Large Language Models without Training (2025.acl-long)

Challenge:	Existing defenses against jailbreaks focus on perturbing or inspecting inputs, but ignore competing objectives, the underlying cause of alignment failures.
Approach:	They propose a novel defense that employs adaptive decoding to address the root causes of jailbreak issues.
Outcome:	The proposed defense improves safety alignment while maintaining helpfulness.

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Cong Liu, Xiaojun Quan, Yan Pan, Weigang Wu, Xu Chen, Liang Lin

Exploring End-to-End Differentiable Natural Logic Modeling (2020.coling-main)

Challenge:	Cool-Fusion is a simple yet effective approach to combine two or more heterogeneous large language models .
Approach:	They propose a method that fuses the knowledge of two or more heterogeneous large language models to leverage complementary strengths.
Outcome:	The proposed method increases accuracy from three strong source LLMs on GSM8K by 17.4%.

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Yufei Feng, Zi’ou Zheng, Quan Liu, Michael Greenspan, Xiaodan Zhu

Low-Resource Generation of Multi-hop Reasoning Questions (2020.acl-main)

Challenge:	Existing approaches to integrate natural logic with neural networks are brittle and prone to fail in the presence of noise and uncertainty.
Approach:	They propose to integrate natural logic with neural networks to create differentiable models that integrate natural reasoning with subsymbolic vector representations and neural components.
Outcome:	The proposed model can model monotonicity-based reasoning, compared to baseline models without inductive bias.

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Jianxing Yu, Wei Liu, Shuang Qiu, Qinliang Su, Kai Wang, Xiaojun Quan, Jian Yin

Low-Resource NLP

Challenge:	Existing methods to generate valid and fluent questions from text are limited and insufficient for training.
Approach:	They propose to generate multi-hop reasoning questions from the raw text in a low resource circumstance by deducing over multiple relations on several sentences in the text.
Outcome:	The proposed model can be applied to the task of machine reading comprehension and achieve significant performance improvements.

Fine-grained Knowledge Enhancement for Retrieval-Augmented Generation (2025.findings-acl)

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Jingxuan Han, Zhendong Mao, Yi Liu, Yexuan Che, Zheren Fu, Quan Wang

Information Retrieval

Challenge:	Existing studies rely on semantic similarity to retrieve knowledge but ignore fine-grained information within documents.
Approach:	They propose a fine-grained knowledge enhancement method to fill knowledge gaps with retrieved external information by a Chain-of-Thought prompting procedure and a decoding enhancement strategy to constrain the document-based decoding process.
Outcome:	The proposed method can be applied in a plug-and-play manner to enhance its performance with no additional modules or training process.

PEPE: Long-context Extension for Large Language Models via Periodic Extrapolation Positional Encodings (2025.findings-emnlp)

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Jikun Hu, Dongsheng Guo, Yuli Liu, Qingyao Ai, Lixuan Wang, Xuebing Sun, Qilei Zhang, Quan Zhou, Cheng Luo

RPO: Retrieval Preference Optimization for Robust Retrieval-Augmented Generation (2025.acl-long)

Challenge:	Long-context extension attempts to extend contextual window in pre-trained LLMs . primary method involves expanding initial positional encodings, disrupting positional learning .
Approach:	They propose a new extension strategy based on Rotary Position Embedding to extend contextual window in pre-trained large language models.
Outcome:	The proposed method can extend the contextual window in pre-trained large language models . expansion disrupts positional encodings learned during pre-training, authors show .

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Shi-Qi Yan, Quan Liu, Zhen-Hua Ling

Information Retrieval

Challenge:	Large language models struggle to evaluate the correctness of non-parametric knowledge when it differs from internal memorization, leading to knowledge conflicts during response generation.
Approach:	They propose a lightweight alignment method to leverage multi-source knowledge based on retrieval relevance.
Outcome:	Experiments on four datasets show that the proposed method outperforms RAG by 4-10% in accuracy without any extra component.

MUSTIE: Multimodal Structural Transformer for Web Information Extraction (2023.acl-long)

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Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Zenglin Xu, Shaoliang Nie, Sinong Wang, Madian Khabsa, Hamed Firooz, Dongfang Liu

Retrieval-Augmented Parsing for Complex Graphs by Exploiting Structure and Uncertainty (2023.findings-emnlp)

Challenge:	Recent sequential modeling approaches focus on extracting information from textual sources while ignoring rich information from other modalities such as image and web layout.
Approach:	They propose a novel MUltimodal Structural Transformer that integrates multiple modalities for web information extraction.
Outcome:	The proposed model outperforms existing methods on WebSRC and Common Crawl benchmarks.

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Zi Lin, Quan Yuan, Panupong Pasupat, Jeremiah Liu, Jingbo Shang

Structured Data in NLP

Challenge:	Retrieval augmentation is effective for large graph parsing tasks, but can fail to identify the most informative exemplars . structure-aware and uncertainty-guided adaptive retrieval (SUGAR) exploits two unique sources of information: structural similarity and model uncertainty.
Approach:	They propose a structure-aware and uncertainty-guided adaptive retrieval approach that exploits structural similarity and model uncertainty to improve retrieval-augmented parsing for complex graph problems.
Outcome:	The proposed method improves retrieval-augmented parsing for graph parsers with large output graphs and non-trivial structure.

Filtering before Iteratively Referring for Knowledge-Grounded Response Selection in Retrieval-Based Chatbots (2020.findings-emnlp)

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Jia-Chen Gu, Zhenhua Ling, Quan Liu, Zhigang Chen, Xiaodan Zhu

Wider & Closer: Mixture of Short-channel Distillers for Zero-shot Cross-lingual Named Entity Recognition (2022.emnlp-main)

Challenge:	Experimental results show that FIRE outperforms previous methods for building knowledge-grounded retrieval-based chatbots . a method called Filtering before iteratively referring is used to ground a conversation on background knowledge .
Approach:	They propose a method for grounding conversation on background knowledge . they use context filter and knowledge filter to make context and knowledge aware . experimental results show that FIRE outperforms previous methods .
Outcome:	The proposed method outperforms previous methods on two datasets.

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Jun-Yu Ma, Beiduo Chen, Jia-Chen Gu, Zhenhua Ling, Wu Guo, Quan Liu, Zhigang Chen, Cong Liu

Random Entity Quantization for Parameter-Efficient Compositional Knowledge Graph Representation (2023.emnlp-main)

Challenge:	Existing mainstream methods for zero-shot cross-lingual named entity recognition ignore the rich and complementary information lying in the intermediate layers of pre-trained language models and domain-invariant information is easily lost during transfer.
Approach:	They propose a mixture of short-channel distillers to fully interact the rich hierarchical information in the teacher model and to transfer knowledge to the student model sufficiently and efficiently.
Outcome:	The proposed method shows great generalization and compatibility across languages and fields.

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Jiaang Li, Quan Wang, Yi Liu, Licheng Zhang, Zhendong Mao

Semantic Text Processing

Challenge:	Existing approaches to learning on Knowledge Graphs (KGs) are not critical for learning on KGs.
Approach:	They propose an alternative approach to represent entities by composing entity-corresponding codewords matched from predefined small-scale codebooks.
Outcome:	The proposed approach achieves similar results to existing methods.

ODE Transformer: An Ordinary Differential Equation-Inspired Model for Sequence Generation (2022.acl-long)

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Bei Li, Quan Du, Tao Zhou, Yi Jing, Shuhan Zhou, Xin Zeng, Tong Xiao, JingBo Zhu, Xuebo Liu, Min Zhang

ProactiveEval: A Unified Evaluation Framework for Proactive Dialogue Agents (2026.acl-long)

Challenge:	Residual networks are an Euler discretization of solutions to Ordinary Differential Equations (ODE).
Approach:	They propose a residual block of layers in Transformer that can be described as a higher-order solution to ODE.
Outcome:	The proposed architecture can gain large improvements over strong baselines at a slight cost in inference efficiency.

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Tianjian Liu, Fanqi Wan, Jiajian Guo, Xiaojun Quan

Dialogue Systems & Conversational Agents

Challenge:	Existing studies on proactive dialogue models focus on domain-specific or task-oriented scenarios, which leads to fragmented evaluations and limits the comprehensive exploration of models’ proactive dialogue abilities.
Approach:	They propose a framework for evaluating proactive dialogue capabilities of large language models that decomposes proactive dialogue into target planning and dialogue guidance, establishing evaluation metrics across various domains.
Outcome:	The proposed framework decomposes proactive dialogue into target planning and dialogue guidance, establishing evaluation metrics across various domains, and enables automatic generation of diverse and challenging evaluation data.

MixPAVE: Mix-Prompt Tuning for Few-shot Product Attribute Value Extraction (2023.findings-acl)

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Li Yang, Qifan Wang, Jingang Wang, Xiaojun Quan, Fuli Feng, Yu Chen, Madian Khabsa, Sinong Wang, Zenglin Xu, Dongfang Liu

Double: Breaking the Acceleration Limit via Double Retrieval Speculative Parallelism (2026.acl-long)

Challenge:	Existing methods for product attribute value extraction focus on extracting values for a set of known attributes with sufficient training data.
Approach:	They propose a prompt tuning approach to extract attributes from product information using mixed prompts.
Outcome:	The proposed approach improves on two product benchmarks and shows parameter-efficient training and avoids model overfitting.

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Yuhao Shen, Tianyu Liu, Junyi Shen, Jinyang Wu, Quan Kong, Huan Li, Cong Wang

Information Retrieval

Challenge:	Parallel Speculative Decoding (PSD) has limitations due to speedup limits and high computational waste . a novel synchronous mechanism solves the Retrieval Precision-Efficiency Dilemma .
Approach:	They propose a framework that combines a draft-verification-based approach with a synchronous mechanism to solve the Retrieval Precision-Efficiency Dilemma.
Outcome:	The proposed framework breaks speedup limits for Speculative Decoding by overlapping draft generation with verification.

Program Enhanced Fact Verification with Verbalization and Graph Attention Network (2020.emnlp-main)

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Xiaoyu Yang, Feng Nie, Yufei Feng, Quan Liu, Zhigang Chen, Xiaodan Zhu

Beyond Stochastic Exploration: What Makes Training Data Valuable for Agentic Search (2026.findings-acl)

Challenge:	Existing methods for fact verification based on structured data are challenging and require further study.
Approach:	They propose a program-enhanced verbalization and a graph attention network to integrate programs and execution into textual inference models.
Outcome:	The proposed framework achieves a new state-of-the-art accuracy on a benchmark dataset . it is compared with existing frameworks on symbolic and informal inference models .

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Chuzhan Hao, Wenfeng Feng, Guochao Jiang, Guofeng Quan, Guohua Liu, Yuewei Zhang

APrompt: Attention Prompt Tuning for Efficient Adaptation of Pre-trained Language Models (2023.emnlp-main)

Challenge:	Existing RL-based search agents rely on stochastic exploration, leading to inefficient reasoning trajectories and unstable training.
Approach:	They propose a framework to enhance the performance and training stability of search agents by transforming raw reasoning trajectories into hierarchical experience knowledge.
Outcome:	The proposed framework exhibits strong cross-task and cross-algorithm generalizations on multiple complex agentic search and mathematical reasoning benchmarks.

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Qifan Wang, Yuning Mao, Jingang Wang, Hanchao Yu, Shaoliang Nie, Sinong Wang, Fuli Feng, Lifu Huang, Xiaojun Quan, Zenglin Xu, Dongfang Liu

MADNet: Maximizing Addressee Deduction Expectation for Multi-Party Conversation Generation (2023.emnlp-main)

Challenge:	Existing prompt tuning methods only introduce prompts at the input layer, limiting performance and leaving large room for improvement.
Approach:	They propose a method that involves tuning a small set of soft prompts for pre-trained language models.
Outcome:	The proposed method outperforms state-of-the-art methods with pre-trained models on the SuperGLUE benchmark.

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Jia-Chen Gu, Chao-Hong Tan, Caiyuan Chu, Zhen-Hua Ling, Chongyang Tao, Quan Liu, Cong Liu

D-NET: A Pre-Training and Fine-Tuning Framework for Improving the Generalization of Machine Reading Comprehension (D19-58)

Challenge:	Existing methods for multi-party conversations rely on addressee labels and can only be applied to an ideal setting where addresses are missing.
Approach:	They propose a method that maximizes addressee deduction expectation in heterogeneous graph neural networks for MPC generation.
Outcome:	The proposed method outperforms baseline models on Ubuntu IRC channel benchmarks on the task of MPC generation under a common and challenging setting where addressee labels are missing.

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Hongyu Li, Xiyuan Zhang, Yibing Liu, Yiming Zhang, Quan Wang, Xiangyang Zhou, Jing Liu, Hua Wu, Haifeng Wang

Qwen2.5-xCoder: Multi-Agent Collaboration for Multilingual Code Instruction Tuning (2025.acl-long)

Challenge:	MRC requires machines to understand text and answer questions about the text.
Approach:	They propose a simple system Baidu submitted for MRQA 2019 Shared Task that focused on generalization of machine reading comprehension (MRC) models.
Outcome:	The proposed system is ranked at top 1 of all participants in terms of averaged F1 score.

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Jian Yang, Wei Zhang, Yibo Miao, Shanghaoran Quan, Zhenhe Wu, Qiyao Peng, Liqun Yang, Tianyu Liu, Zeyu Cui, Binyuan Hui, Junyang Lin

Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic (2023.acl-long)

Challenge:	Existing methods to train code LLMs view each programming language in isolation . experimental results show that Qwen2.5-xCoder can bridge the gap between different programming languages .
Approach:	They propose a framework that allows agents to collaborate to enhance multilingual instruction tuning for code LLMs.
Outcome:	Experimental results show that Qwen2.5-xCoder can transfer knowledge efficiently and effectively between languages.

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Connor Pryor, Quan Yuan, Jeremiah Liu, Mehran Kazemi, Deepak Ramachandran, Tania Bedrax-Weiss, Lise Getoor

Dialogue Systems & Conversational Agents

Challenge:	Existing DSI approaches infer latent dialog structure without access to domain knowledge.
Approach:	They propose a neural-symbolic approach that injects symbolic knowledge into latent space of a generative neural model.
Outcome:	The proposed approach boosts performance over the canonical baselines over three dialog structure induction datasets.

Shallow-to-Deep Training for Neural Machine Translation (2020.emnlp-main)

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Bei Li, Ziyang Wang, Hui Liu, Yufan Jiang, Quan Du, Tong Xiao, Huizhen Wang, Jingbo Zhu

Multilinguality

Challenge:	Experimental results show that deep training is 1:4 faster than training from scratch.
Approach:	They propose a shallow-to-deep training method that learns deep models by stacking shallow models.
Outcome:	The proposed method is 1:4 faster than training from scratch and achieves BLEU scores of 30:33 and 43:29 on two translation tasks.

Learning to Generate Question by Asking Question: A Primal-Dual Approach with Uncommon Word Generation (2022.emnlp-main)

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Qifan Wang, Li Yang, Xiaojun Quan, Fuli Feng, Dongfang Liu, Zenglin Xu, Sinong Wang, Hao Ma

Text Generation

Challenge:	Existing automatic question generation methods focus on encoding passage and answer to generate question.
Approach:	They propose an automatic question generation approach which integrates question generation with its dual problem, question answering, into a unified primal-dual framework.
Outcome:	The proposed approach outperforms existing methods on SQuAD and HotpotQA benchmarks.

FlipGuard: Defending Preference Alignment against Update Regression with Constrained Optimization (2024.emnlp-main)

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Mingye Zhu, Yi Liu, Quan Wang, Junbo Guo, Zhendong Mao

Challenge:	Recent advances in preference alignment have significantly improved Large Language Models' ability to generate texts that align with human preferences and values.
Approach:	They propose a constrained optimization approach to detect and mitigate update regression with focal attention.
Outcome:	The proposed approach detects and mitigates update regression with focal attention while maintaining excellent overall performance.

GIFT: Graph-Induced Fine-Tuning for Multi-Party Conversation Understanding (2023.acl-long)

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Jia-Chen Gu, Zhenhua Ling, Quan Liu, Cong Liu, Guoping Hu

Step Potential Advantage Estimation: Harnessing Intermediate Confidence and Correctness for Efficient Mathematical Reasoning (2026.findings-acl)

Challenge:	Existing methods on understanding multi-party conversations typically embed interlocutors and utterances into sequential information flows or use superficial graph structures.
Approach:	They propose a plug-and-play method which adapts Transformer-based pre-trained language models for universal MPC understanding.
Outcome:	The proposed method can adapt Transformer-based pre-trained language models for universal MPC understanding.

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Fei Wu, Zhenrong Zhang, Qikai Chang, Jianshu Zhang, Quan Liu, Jun Du

EconLogicQA: A Question-Answering Benchmark for Evaluating Large Language Models in Economic Sequential Reasoning (2024.findings-emnlp)

Challenge:	Existing approaches to RLVR provide sparse supervision since reward arrives only after the full generation is complete.
Approach:	They propose a step-level reward system that extracts confidence and correctness and combines them into a Step Potential signal that explicitly estimates reasoning state at each step.
Outcome:	The proposed method outperforms existing methods on multiple benchmarks and improves accuracy while reducing response length.

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Yinzhu Quan, Zefang Liu

Question Answering

Challenge:	EconLogicQA requires models to discern and sequence multiple interconnected events, capturing the complexity of economic logics.
Approach:	They propose a benchmark to assess the sequential reasoning capabilities of large language models (LLMs) EconLogicQA requires models to discern and sequence multiple interconnected events, capturing the complexity of economic logics.
Outcome:	The proposed benchmark is based on a set of multi-event scenarios derived from economic articles and evaluates it across leading-edge LLMs.

A Pretraining Numerical Reasoning Model for Ordinal Constrained Question Answering on Knowledge Base (2021.findings-emnlp)

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Yu Feng, Jing Zhang, Gaole He, Wayne Xin Zhao, Lemao Liu, Quan Liu, Cuiping Li, Hong Chen

Semantic Text Processing

Challenge:	Existing knowledge bases (KBs) can explicitly facilitate the QA process.
Approach:	They propose a numerical reasoning model pretraining NumGNN and NumTransformer, guided by explicit self-supervision signals, to enhance numerical reasoning ability for IR-based KBQA models.
Outcome:	Extensive experiments on two KBQA benchmarks confirm the effectiveness of the proposed model.

EquivPruner: Boosting Efficiency and Quality in LLM-Based Search via Action Pruning (2026.acl-long)

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Jiawei Liu, Qisi Chen, Jianshu Zhang, Quan Liu, Defu Lian

RouteLMT: Learned Sample Routing for Hybrid LLM Translation Deployment (2026.acl-industry)

Challenge:	Existing semantic similarity methods struggle to accurately identify semantically equivalent steps in domain-specific contexts like mathematical reasoning.
Approach:	They propose a simple yet effective approach that identifies and prunes semantically equivalent actions during LLM reasoning search.
Outcome:	The proposed approach reduces token consumption while improving reasoning efficiency and accuracy on Qwen2.5-Math-7B-Instruct on GSM8K.

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Yingfeng Luo, Hongyu Liu, DingYang Lin, Kaiyan Chang, Chenglong Wang, Bei Li, Quan Du, Tong Xiao, JingBo Zhu

Machine Translation

Challenge:	Existing routing strategies rely on heuristics, external predictors, or absolute quality estimation to capture whether the large model provides a worthwhile improvement over the small one.
Approach:	They propose a budget allocation problem for routing large model to large model . they propose heuristics, external predictors, or absolute quality estimation to determine the optimal signal for budgeted decisions.
Outcome:	The proposed model outperforms heuristics, quality/difficulty estimation baselines and achieves a superior quality–budget Pareto frontier.

Chain-of-Question: A Progressive Question Decomposition Approach for Complex Knowledge Base Question Answering (2024.findings-acl)

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Peng Yixing, Quan Wang, Licheng Zhang, Yi Liu, Zhendong Mao

Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots (D19-1)

Challenge:	Existing methods to answer complex questions rely on decomposition of complex questions into sub-questions . Existing approaches to decompose complex questions are limited by the original question .
Approach:	They propose a question decomposition approach to decompose semantically clear questions . they use the decomposed sub-questions to select relevant patterns as auxiliary information .
Outcome:	The proposed method achieves state-of-the-art performance on multiple datasets.

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Jia-Chen Gu, Zhen-Hua Ling, Xiaodan Zhu, Quan Liu

Enhancing Pre-Trained Language Representations with Rich Knowledge for Machine Reading Comprehension (P19-1)

Challenge:	Existing models for personalized dialogues rank responses according to their semantic relevance with the given context.
Approach:	They propose a dually interactive matching network (DIM) for presenting personalities of dialogue agents in retrieval-based chatbots.
Outcome:	The proposed model outperforms the existing model by 14.5% and 27.7% on a PERSONA-CHAT dataset.

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An Yang, Quan Wang, Jing Liu, Kai Liu, Yajuan Lyu, Hua Wu, Qiaoqiao She, Sujian Li

Semantic Text Processing

Challenge:	Recent results show pre-trained language models (LMs) can improve machine reading comprehension (MRC) Experimental results indicate that KT-NET offers significant and consistent improvements over BERT .
Approach:	They propose a method that leverages external knowledge bases to improve machine reading comprehension (MRC) KT-NET employs an attention mechanism to select desired knowledge from KBs and fuses selected knowledge with BERT to enable context- and knowledge-aware predictions.
Outcome:	The proposed model outperforms baseline models on ReCoRD and SQuAD1.1 benchmarks and ranks 1st on the ReCoDR and SQUAD1.1 leaderboards.

Detecting Speaker Personas from Conversational Texts (2021.emnlp-main)

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Jia-Chen Gu, Zhenhua Ling, Yu Wu, Quan Liu, Zhigang Chen, Xiaodan Zhu

Dialogue Systems & Conversational Agents

Challenge:	Existing studies on personas are pre-defined and hard to obtain before a conversation . a new task aims to detect speaker persona based on conversational text .
Approach:	They propose a task to detect speaker personas based on conversational text . they build a dataset for SPD and propose utterance-to-profile matching networks .
Outcome:	The proposed task outperforms baseline models and utterance-to-profile (U2P) matching networks.

Structured Optimal Brain Pruning for Large Language Models (2024.emnlp-main)

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Jiateng Wei, Quan Lu, Ning Jiang, Siqi Li, Jingyang Xiang, Jun Chen, Yong Liu