Papers by Juntao Li

Towards Better Hierarchical Text Classification with Data Generation (2023.findings-acl)

Challenge:	Recent years have witnessed remarkable progress in large language models (LLMs).
Approach:	They propose a framework for contrastive decoding to enhance instruction-tuned models.
Outcome:	The proposed framework improves model performance without additional data or computational resources.

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Yue Wang, Dan Qiao, Juntao Li, Jinxiong Chang, Qishen Zhang, Zhongyi Liu, Guannan Zhang, Min Zhang

Information Extraction & Text Mining

Challenge:	Existing methods to improve hierarchical text classification are expensive and lack high-quality labeled data.
Approach:	They propose a hierarchical text classification framework that can achieve both label controllability and text diversity by extracting high-quality hierarchic label information.
Outcome:	The proposed method can achieve label controllability and text diversity by extracting high-quality hierarchical label information.

An Empirical Study of Iterative Refinements for Non-autoregressive Translation (2025.acl-long)

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Yisheng Xiao, Pei Guo, Zechen Sun, Juntao Li, Kai Song, Min Zhang

Retrieval and Reasoning on KGs: Integrate Knowledge Graphs into Large Language Models for Complex Question Answering (2024.findings-emnlp)

Challenge:	Iterative non-autoregressive (NAR) models have recently demonstrated impressive performance in varied generation tasks, surpassing the autoregressive Transformer.
Approach:	They propose a strategy to conduct efficient refinements without performance declines by using two simple metrics to identify potential problems existing in current refinement processes.
Outcome:	The proposed model outperforms the autoregressive Transformer by around one BLEU on average.

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Yixin Ji, Kaixin Wu, Juntao Li, Wei Chen, Mingjie Zhong, Xu Jia, Min Zhang

Improving Temporal Generalization of Pre-trained Language Models with Lexical Semantic Change (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) have performed impressively in various NLP tasks, but their inherent hallucination phenomena severely challenge their credibility in complex reasoning.
Approach:	They propose to integrate explainable Knowledge Graphs (KGs) with LLMs to alleviate hallucinations . they construct subgraphs to enhance the retrieval capabilities of KGs via CoT reasoning.
Outcome:	Extensive experiments on two KGQA datasets show that the proposed model achieves convincing performance compared to strong baselines.

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Zhaochen Su, Zecheng Tang, Xinyan Guan, Lijun Wu, Min Zhang, Juntao Li

Efficient Continue Training of Temporal Language Model with Structural Information (2023.findings-emnlp)

Challenge:	Existing methods to improve neural language models perform poorly on emerging data.
Approach:	They propose a lexical-level masking strategy to post-train a neural language model using static data from past years.
Outcome:	The proposed method outperforms existing methods on two pre-trained language models, two classification tasks, and four benchmark datasets.

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Zhaochen Su, Juntao Li, Zikang Zhang, Zihan Zhou, Min Zhang

Towards Advanced Mathematical Reasoning for LLMs via First-Order Logic Theorem Proving (2025.emnlp-main)

Challenge:	Existing temporal language models are limited by the superficial temporal information brought by timestamps, which fails to learn the inherent changes of linguistic components.
Approach:	They propose a method that captures syntactically changed tokens and captures the relationship between the time prefix and tokens.
Outcome:	The proposed method outperforms existing temporal language models on two datasets and three tasks.

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Chuxue Cao, Mengze Li, Juntao Dai, Jinluan Yang, Zijian Zhao, Shengyu Zhang, Weijie Shi, Chengzhong Liu, Sirui Han, Yike Guo

Revealing and Mitigating the Local Pattern Shortcuts of Mamba (2025.findings-acl)

Challenge:	Large language models (LLMs) have shown promising first-order logic (FOL) reasoning capabilities with applications in various areas, but their effectiveness in complex mathematical reasoning involving multi-step FOL deductions remains under-explored.
Approach:	They propose a self-adaptive solution that enhances the Diversity and REAsonability of LLMs’ generation strategies by introducing an Axiom-Driven Strategy Diversification mechanism and a Sub-Proposition Error Feedback to help LLM reflect on and correct their proofs.
Outcome:	The proposed model improves diversity and REAsonability of LLMs’ generation strategies by introducing an Axiom-Driven Strategy Diversification mechanism and a Sub-Proposition Error Feedback to help LLM reflect on and correct proofs.

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WangJie You, Zecheng Tang, Juntao Li, Lili Yao, Min Zhang

IS-CoT: Breaking the Long-form Generation Collapse via Interleaved Structural Thinking (2026.acl-long)

Challenge:	Recent studies show that Mamba excels in tasks that involve localized key information but faces challenges with tasks that require handling distributed key information.
Approach:	They propose to introduce a global gate module into Mamba to address this problem by adding 4M extra parameters to the model.
Outcome:	The proposed model outperforms attention-based models on synthetic and synthetic tasks with only 4M extra parameters.

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Zechen Sun, Yuyang Sun, Zecheng Tang, Juntao Li, Wenpeng Hu, Wenliang Chen, Zhunchen Luo, Guotong Geng, Min Zhang

Dynamic and Efficient Inference for Text Generation via BERT Family (2023.acl-long)

Challenge:	Existing models with reasoning capabilities suffer from a severe length collapse in open-ended writing .
Approach:	They propose a framework that embeds a dynamic plan-write-reflect cycle into the generation process and train a model with interleaved reasoning traces.
Outcome:	The proposed framework achieves state-of-the-art performance on long-form benchmarks compared to other models on the same dataset.

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Xiaobo Liang, Juntao Li, Lijun Wu, Ziqiang Cao, Min Zhang

MMA: Cross-Domain Knowledge Integration via Mixture of Multi-Domain Agents (2025.findings-emnlp)

Challenge:	Existing methods to improve performance of pre-trained language models are limited due to large-scale parameters and the universal autoregressive decoding paradigm.
Approach:	They propose a novel fine-tuning method which can make a single pre-trained model support Dynamic and Efficient infERence and achieve an adaptive trade-off between model performance and latency.
Outcome:	The proposed method achieves higher BLEU scores than the strong autoregressive Transformer model on translation tasks with 3 12 times speedup and faster inference speed compared with the BART model on four GLGE benchmark tasks.

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Kehang Jia, Juntao Li, Xiaobo Liang, Yisheng Xiao, Yixuan Yang, Min Zhang

Challenge:	achieving synergistic improvements between generalization and domain specialization remains a challenge in pre-training and post-training.
Approach:	They propose a test-time cross-domain knowledge integration method that integrates general-purpose and domain-specific models to enhance their performance on complex, domainspecific tasks.
Outcome:	The proposed method combines the outputs of general-purpose and domain-specific models to improve their performance on complex, domainspecific tasks.

Early Exit with Disentangled Representation and Equiangular Tight Frame (2023.findings-acl)

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Yixin Ji, Jikai Wang, Juntao Li, Qiang Chen, Wenliang Chen, Min Zhang

Representation Learning

Challenge:	Existing early exit paradigm relies on training parametrical internal classifiers to complete specific tasks.
Approach:	They propose a method to decouple two distinct types of representation and introduce a non-parametric tight frame classifier for improvement.
Outcome:	Experiments on monolingual and multilingual tasks show that the proposed method improves over existing methods.

Exploring Reversal Mathematical Reasoning Ability for Large Language Models (2024.findings-acl)

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Pei Guo, WangJie You, Juntao Li, Yan Bowen, Min Zhang

Gated Mechanism Enhanced Multi-Task Learning for Dialog Routing (2022.coling-1)

Challenge:	Large language models (LLMs) have been a success in the wide range of natural language understanding and reasoning tasks.
Approach:	They propose a training method to improve general and reversal reasoning abilities by using a reversed dataset.
Outcome:	The proposed method improves general and reversal reasoning abilities and alleviates the reverse curse.

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Ziming Huang, Zhuoxuan Jiang, Ke Wang, Juntao Li, Shanshan Feng, Xian-Ling Mao

Low-Resource NLP

Challenge:	Existing methods for dialog routing are mostly heuristic and cannot achieve high-quality performance.
Approach:	They propose a multi-task learning framework with a dialog encoder and two tailored gated mechanism modules to solve this problem.
Outcome:	The proposed model can play the role of hierarchical information filtering and is non-invasive to existing dialog systems.

Can Diffusion Model Achieve Better Performance in Text Generation ? Bridging the Gap between Training and Inference ! (2023.findings-acl)

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Zecheng Tang, Pinzheng Wang, Keyan Zhou, Juntao Li, Ziqiang Cao, Min Zhang

Text Generation

Challenge:	Existing models for text generation use a discrete data embedding module to map the data into the continuous space.
Approach:	They propose two methods to bridge the gap between training and inference by mapping the discrete text into the continuous space.
Outcome:	The proposed methods can achieve 100 200 speedup with better performance on 6 generation tasks.

DUAL RM: Beyond Rule-based Preference Reward Modeling via Meta-Reward (2026.acl-long)

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Xiaobo Liang, Wanfu Wang, Qipeng Huang, Yuyang Ding, Zecheng Tang, Yixin Ji, Qianben Chen, Zhe Zhao, Kehai Chen, Juntao Li, Min Zhang

Challenge:	Existing preference-based reward modeling methods face a recursive dependency where each verifier requires a meta-verifier, leading to continuous and costly dependence on human annotation.
Approach:	They propose a dual RM that couples discriminative and generative reward models under a non-parametric meta-reward.
Outcome:	The proposed model achieves strong performance across major preference benchmarks and even when trained exclusively on language modality, it exhibits robust cross-modal transfer on Omni-RewardBench.

Unleashing LLM Reasoning Capability via Scalable Question Synthesis from Scratch (2025.acl-long)

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Yuyang Ding, Xinyu Shi, Xiaobo Liang, Juntao Li, Zhaopeng Tu, Qiaoming Zhu, Min Zhang

Crossing the Reward Bridge: Expanding Reinforcement Learning with Verifiable Rewards Across Diverse Domains (2026.acl-long)

Challenge:	Existing methods to improve the mathematical reasoning capabilities of Large Language Models (LLMs) are limited due to the proprietary nature of the data.
Approach:	They propose a data synthesis method that generates large-scale mathematical reasoning datasets using lightweight 7B-scale models.
Outcome:	The proposed method outperforms existing open-source datasets in both in-domain and out-of-domain evaluations and shows improvements in code reasoning tasks.

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Yi Su, Dian Yu, Linfeng Song, Juntao Li, Haitao Mi, Zhaopeng Tu, Min Zhang, Dong Yu

Challenge:	Reinforcement learning with verifiable rewards (RLVR) has been effective on structured tasks, but its reliance on simple, rule-based verifiers creates a bottleneck.
Approach:	They propose a framework that uses a generative verifier to provide soft, probabilistic rewards.
Outcome:	The proposed framework outperforms existing models up to 10x their size and can be scalable and effective.

Efficient Domain Adaptation for Non-Autoregressive Machine Translation (2024.findings-acl)

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WangJie You, Pei Guo, Juntao Li, Kehai Chen, Min Zhang

Accurate KV Cache Quantization with Outlier Tokens Tracing (2025.acl-long)

Challenge:	Existing non-parametric approaches like nearest neighbor machine translation have made small Autoregressive translation models less efficient . despite their impressive generalization and task performance, LLMs suffer from prohibitive inference cost when confronted with specific domains.
Approach:	They propose a domain adaptation approach that tailors a k-nearest-neighbor algorithm for NAT models that incorporates the parallel nature of NAT.
Outcome:	The proposed approach achieves significant improvements over the Base-NAT model and exhibits enhanced efficiency.

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Yi Su, Yuechi Zhou, Quantong Qiu, Juntao Li, Qingrong Xia, Ping Li, Xinyu Duan, Zhefeng Wang, Min Zhang

Efficient Reasoning for LLMs through Speculative Chain-of-Thought (2026.findings-acl)

Challenge:	Large Language Models (LLMs) require substantial computational resources during deployment.
Approach:	They propose a method to identify outlier tokens and exclude them from quantization . they find that the method can deliver a 6.4 times reduction in memory usage and a 2.5 times increase in throughput .
Outcome:	The proposed method delivers a 6.4 times reduction in memory usage and a 2.5 times increase in throughput under 2-bit quantization.

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Jikai Wang, Juntao Li, Jianye Hou, Yan Bowen, Lijun Wu, Min Zhang

Adaptive Feature-based Low-Rank Compression of Large Language Models via Bayesian Optimization (2024.findings-emnlp)

Challenge:	Existing methods for efficient reasoning focus on reducing the number of model parameters or shortening the chain-of-thought length.
Approach:	They propose a speculative chain-of-thought (SCoT) method to reduce reasoning latency by accelerating average reasoning speed through large and small model collaboration.
Outcome:	The proposed method reduces reasoning latency by 48%66% and 21%49% on GSM8K, MATH, GaoKao, CollegeMath and Olympiad datasets.

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Yixin Ji, Yang Xiang, Juntao Li, Qingrong Xia, Zi Ye, Xinyu Duan, Zhefeng Wang, Kehai Chen, Min Zhang

When Is Thinking Enough? Early Exit via Sufficiency Assessment for Efficient Reasoning (2026.acl-long)

Challenge:	Large language models require a balance between efficiency and performance.
Approach:	They propose a low-rank compression technique that reduces non-essential parameters by decomposing weight matrices into products of two low-ranked matrici.
Outcome:	The proposed method outperforms existing pruning and low-rank compression techniques in maintaining model performance at the same compression ratio.

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Yang Xiang, Yixin Ji, Ruotao Xu, Dan Qiao, Zheming Yang, Juntao Li, Min Zhang

Beyond Task-Level Context: Class-Conditional Context Vectors for Implicit In-Context Learning (2026.findings-acl)

Challenge:	Existing approaches to early exit reasoning often rely on handcrafted or empirical indicators that are unreliable and impractical.
Approach:	They propose a framework that allows LRMs to assess the sufficiency of its chain-of-thought and determine the optimal point for early exit.
Outcome:	The proposed framework reduces reasoning length by 28.9%–34.9% with minimal performance loss, effectively mitigating overthinking.

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Jianxin Zhang, Yilu Hu, Teng Liu, Pei Guo, Juntao Li

Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study (2021.naacl-main)

Challenge:	Existing approaches aggregate demonstrations from all classes into a shared, task-level context vector, capturing global task information but without explicitly preserving fine-grained, class-conditional semantic distinctions.
Approach:	They propose a class-conditional context vector extension to implicit in-context learning that explicitly models class-specific contextual information by constructing separate context vectors for each class.
Outcome:	The proposed extension outperforms task-level context vector baselines and achieves higher average accuracy than conventional few-shot learning on most models.

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Chongyang Tao, Shen Gao, Juntao Li, Yansong Feng, Dongyan Zhao, Rui Yan

Challenge:	Existing approaches to encode natural languages without orders are lacking.
Approach:	They conduct a comprehensive analysis of the ability of neural models to organize sentences from a bag of words under three typical scenarios.
Outcome:	The proposed models can reorder or reconstruct sentences from a bag of words under three typical scenarios.

SelfMix: Robust Learning against Textual Label Noise with Self-Mixup Training (2022.coling-1)

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Dan Qiao, Chenchen Dai, Yuyang Ding, Juntao Li, Qiang Chen, Wenliang Chen, Min Zhang

Information Extraction & Text Mining

Challenge:	Existing methods to handle label noise in text classification tasks are limited to visual data.
Approach:	They propose a method to handle label noise in text classification tasks using a Gaussian Mixture Model.
Outcome:	The proposed method outperforms baselines on three types of text classification tasks on visual and textual data.

Demonstration Augmentation for Zero-shot In-context Learning (2024.findings-acl)

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Yi Su, Yunpeng Tai, Yixin Ji, Juntao Li, Yan Bowen, Min Zhang

Low-Resource NLP

Challenge:	Large Language Models (LLMs) have demonstrated an impressive capability known as In-context Learning (ICL), which enables them to acquire knowledge from textual demonstrations without the need for parameter updates.
Approach:	They propose to use model’s previously predicted historical samples as demonstrations for subsequent ones to improve model’ s performance.
Outcome:	The proposed method significantly outperforms the previous method and its predecessors in terms of inference cost and time.

Alignment-Augmented Speculative Decoding with Alignment Sampling and Conditional Verification (2025.emnlp-main)

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Jikai Wang, Zhenxu Tian, Juntao Li, Qingrong Xia, Xinyu Duan, Zhefeng Wang, Baoxing Huai, Min Zhang

Challenge:	Existing methods to accelerate autoregressive generation of large language models require training costs.
Approach:	They propose a training-free alignment-augmented speculative decoding algorithm . it leverages the output distribution obtained in the prefilling phase to provide more aligned draft candidates .
Outcome:	The proposed method increases the average generation score by 3.3 points for the LLaMA3 model.

SafeMT: Multi-turn Safety for Multimodal Language Models (2026.acl-long)

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Han Zhu, Juntao Dai, Jiaming Ji, Haoran Li, Chengkun Cai, Pengcheng Wen, Chi-Min Chan, Boyuan Chen, Yaodong Yang, Sirui Han, Yike Guo

CORE: Cooperative Training of Retriever-Reranker for Effective Dialogue Response Selection (2023.acl-long)

Challenge:	Multi-turn dialogues pose a greater risk than single prompts, but existing safety benchmarks do not account for this situation.
Approach:	They propose a benchmark that features dialogues of varying lengths generated from harmful queries accompanied by images.
Outcome:	The proposed model reduces multi-turn Attack Success Rate (ASR) compared to existing guard models.

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Chongyang Tao, Jiazhan Feng, Tao Shen, Chang Liu, Juntao Li, Xiubo Geng, Daxin Jiang

CMD: a framework for Context-aware Model self-Detoxification (2024.emnlp-main)

Challenge:	Existing methods to train retrieval-based dialogue systems are suboptimal . existing methods to optimize retrieval and rerank modules are sub-optimal, causing sub-optimum performance.
Approach:	They propose a retrieval-based dialogue system with a fast retriever and a smart response reranker that combine the best of both worlds.
Outcome:	The proposed method can learn from each other and evolve together . it can be used in industrial applications and has powered industrial applications.

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Zecheng Tang, Keyan Zhou, Juntao Li, Yuyang Ding, Pinzheng Wang, Yan Bowen, Renjie Hua, Min Zhang

Beware of Model Collapse! Fast and Stable Test-time Adaptation for Robust Question Answering (2023.emnlp-main)

Challenge:	Existing methods of text detoxification fail to achieve a decent balance between effectiveness and generation quality.
Approach:	They propose a text detoxification framework that pays attention to both context and detoxification process.
Outcome:	Experiments on various LLMs show that the proposed framework can yield the best performance compared to baselines.

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Yi Su, Yixin Ji, Juntao Li, Hai Ye, Min Zhang

Robustness in NLP

Challenge:	Pre-trained language models (PLMs) have achieved great success in question answering, but their robustness is insufficient to support their practical applications.
Approach:	They propose a method which regularizes the model's output and an efficient side block to reduce its inference time.
Outcome:	The proposed method achieves comparable or better results than previous TTA methods at a speed close to vanilla forward propagation, which is 1.8 to 4.4 speedup compared to previous methods.

INFORM : Information eNtropy based multi-step reasoning FOR large language Models (2023.emnlp-main)

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Chuyue Zhou, Wangjie You, Juntao Li, Jing Ye, Kehai Chen, Min Zhang

Generative Reward Modeling via Synthetic Criteria Preference Learning (2025.acl-long)

Challenge:	Large language models (LLMs) have demonstrated exceptional performance with dedicated Chain-of-Thought (CoT) prompts.
Approach:	They propose a new method by introducing information entropy as a criteria on for CoT prompt selection.
Outcome:	The proposed model outperforms existing models on seven reasoning benchmarks using two language models.

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Xiaobo Liang, Haoke Zhang, Juntao Li, Kehai Chen, Qiaoming Zhu, Min Zhang

Generating Classical Chinese Poems via Conditional Variational Autoencoder and Adversarial Training (D18-1)

Challenge:	Generative Reward Models (GenRMs) leverage synthesized Chains of Thought (CoT) but this approach introduces risks of overoptimization due to the inability to guarantee the correctness of the CoTs.
Approach:	They propose a criteria-based preference tree for GenRMs that uses chain of thought to generate reasoning . they show that synthesized data can be learned using a long CoT format .
Outcome:	The proposed model shows significant improvements over baselines on multiple human preference benchmarks.

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Juntao Li, Yan Song, Haisong Zhang, Dongmin Chen, Shuming Shi, Dongyan Zhao, Rui Yan

Robustness in NLP

Challenge:	Existing models for automatic poetry generation lack term novelty and thematic consistency.
Approach:	They propose a conditional variational autoencoder with adversarial training for classical Chinese poem generation.
Outcome:	The proposed model outperforms existing models on a large poetry corpus on 'classical Chinese' . it generates poems with novel terms and learns their thematic consistency with their titles.

OPT-Tree: Speculative Decoding with Adaptive Draft Tree Structure (2025.tacl-1)

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Jikai Wang, Yi Su, Juntao Li, Qingrong Xia, Zi Ye, Xinyu Duan, Zhefeng Wang, Min Zhang

Towards More Realistic Chinese Spell Checking with New Benchmark and Specialized Expert Model (2024.lrec-main)

Challenge:	Autoregressive language models generate one token in one step, limiting inference efficiency . Existing methods do not adapt to different situations to maximize acceptance length . speculative decoding has shown great potential for lossless acceleration .
Approach:	They propose an algorithm to construct adaptive and scalable draft trees for autoregressive language models.
Outcome:	Experimental results show that OPT-Tree outperforms existing draft trees and achieves speed-up ratio of up to 3.2 compared with autoregressive decoding.

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Yue Wang, Zilong Zheng, Juntao Li, Zhihui Liu, Jinxiong Chang, Qishen Zhang, Zhongyi Liu, Guannan Zhang, Min Zhang

Syntactic Text Processing

Challenge:	Large Language Models (LLMs) have been gaining attention for their ability to perform a wide range of open-domain tasks . however, the performance of LLMs has yet to be comprehensively evaluated in realistic scenarios .
Approach:	They propose a task to evaluate the performance of Large Language Models (LLMs) they propose RCSC task to convert Chinese text into correct text .
Outcome:	The proposed task evaluates the performance of existing methods in Chinese text . the realistic Chinese spell checker can achieve state-of-the-art performance on the task .

Unveiling the Potential of BERT-family: A New Recipe for Building Scalable, General and Competitive Large Language Models (2025.acl-long)

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Yisheng Xiao, Juntao Li, Wenpeng Hu, Zhunchen Luo, Min Zhang

ALW: Adaptive Layer-Wise contrastive decoding enhancing reasoning ability in Large Language Models (2025.findings-acl)

Challenge:	Generative large language models (LLMs) have significantly influenced various aspects of society, reshaping how we access and interact with information and knowledge.
Approach:	They propose a pre-training task that helps BERT-family excel in wider applications . they also explore the integration of cutting-edge technologies into their models to further enhance their capabilities.
Outcome:	The proposed model exhibits performance levels comparable to current SOTA LLMs across a spectrum of tasks.

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Yuechi Zhou, Chuyue Zhou, Jianxin Zhang, Juntao Li, Min Zhang

Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning? (2024.acl-long)

Challenge:	Existing research has demonstrated that contrast decoding of two different models can improve text quality in open-ended text generation but with limited gains on reasoning tasks.
Approach:	They propose a framework that dynamically disentangles noise in shallow layers from critical signals in deep layers to enhance reasoning ability.
Outcome:	The proposed framework improves answer accuracy while maintaining inference efficiency.

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Zhaochen Su, Juntao Li, Jun Zhang, Tong Zhu, Xiaoye Qu, Pan Zhou, Yan Bowen, Yu Cheng, Min Zhang

Modeling Personalization in Continuous Space for Response Generation via Augmented Wasserstein Autoencoders (D19-1)

Challenge:	Current temporal reasoning datasets are limited to questions about single or isolated events, falling short in mirroring the realistic temporal characteristics involving concurrent nature and intricate temporal interconnections.
Approach:	They propose a co-temporal Question Answering benchmark that contains four co-time scenarios with 4,748 samples for evaluating the co-timing abilities of large language models.
Outcome:	The proposed benchmarks show that current LLMs struggle on CoTempQA tasks even when enhanced with Chain of Thought methodologies.

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Zhangming Chan, Juntao Li, Xiaopeng Yang, Xiuying Chen, Wenpeng Hu, Dongyan Zhao, Rui Yan

Text Generation

Challenge:	Existing work on variableal autoencoders and waterstein autoencoding models has shown significant progress in open-domain response generation.
Approach:	They propose to embed user-level and utterance-level information into two multimodal distributions and combine them into a mixed distribution.
Outcome:	The proposed model outperforms state-of-the-art models on a large-scale real-world dataset.

𝒜3: Automatic Alignment Framework for Attributed Text Generation (2025.acl-long)

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Yue Wang, Haoke Zhang, Juntao Li, Jinxiong Chang, Min Zhang

KBioXLM: A Knowledge-anchored Biomedical Multilingual Pretrained Language Model (2023.findings-emnlp)

Challenge:	Existing approaches of aligning large language models to follow user instructions can lead to undue emphasis on irrelevant documents, which in turn reduces the quality of responses.
Approach:	They propose to use a framework to automatically generate high-quality attributed query-response pairs for both supervised fine-tuning and preference optimization stages without human annotation.
Outcome:	The proposed framework can generate high-quality attributed query-response pairs without human annotation without human intervention.

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Lei Geng, Xu Yan, Ziqiang Cao, Juntao Li, Wenjie Li, Sujian Li, Xinjie Zhou, Yang Yang, Jun Zhang

Multilinguality

Challenge:	Existing models for multilingual biomedical training are monolingual, resulting in limited cross-lingual capability.
Approach:	They propose a model that transforms a multilingual biomedical corpus into a biomedically domain using a knowledge-anchored approach.
Outcome:	The proposed model outperforms monolingual and multilingual models in cross-lingual scenarios.

JANUS: Joint Autoregressive and Non-autoregressive Training with Auxiliary Loss for Sequence Generation (2022.emnlp-main)

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Xiaobo Liang, Lijun Wu, Juntao Li, Min Zhang

Isotropic Representation Can Improve Zero-Shot Cross-Lingual Transfer on Multilingual Language Models (2023.findings-emnlp)

Challenge:	Existing approaches to train autoregressive and non-autoregressive models only consider relevance of model parameters, ignoring correlations between the two manners.
Approach:	They propose a joint autoregressive and non-autoregressive training method using aUxiliary losS to enhance the model performance in both AR and NAR manners simultaneously.
Outcome:	The proposed method improves the model performance in both AR and NAR manners and reduces the inference latency.

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Yixin Ji, Jikai Wang, Juntao Li, Hai Ye, Min Zhang

Automate Strategy Finding with LLM in Quant Investment (2025.findings-emnlp)

Challenge:	Existing mPLMs can align representations well for myriads of cross-lingual transfer tasks.
Approach:	They propose enhanced isotropy and constrained code-switching for zero-shot cross-lingual transfer to alleviate the problem of misalignment caused by anisotropic representations.
Outcome:	The proposed method improves on three zero-shot cross-lingual transfer tasks and over existing methods.

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Zhizhuo Kou, Holam Yu, Junyu Luo, Jingshu Peng, Xujia Li, Chengzhong Liu, Juntao Dai, Lei Chen, Sirui Han, Yike Guo

Are Training Samples Correlated? Learning to Generate Dialogue Responses with Multiple References (P19-1)

Challenge:	Experimental results demonstrate robust performance of the strategy in Chinese & US market regimes compared to established benchmarks.
Approach:	They propose a framework leveraging Large Language Models within a risk-aware multi-agent system for automate strategy finding in quantitative finance.
Outcome:	The proposed framework outperforms all benchmarks in Chinese & US market regimes with 53.17% cumulative return on SSE50.

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Lisong Qiu, Juntao Li, Wei Bi, Dongyan Zhao, Rui Yan

Dialogue Systems & Conversational Agents

Challenge:	Existing approaches to open-domain dialogue generation ignore the nature of 1-to-1 mapping that there may exist multiple valid responses corresponding to the same query.
Approach:	They propose to model open-domain dialogue generation using 1-to-1 mapping . they first extract common features of different responses and then combine them with distinctive features to generate multiple diverse and appropriate responses.
Outcome:	The proposed model outperforms existing models on automatic and human evaluations.

Rethinking Negative Instances for Generative Named Entity Recognition (2024.findings-acl)

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Yuyang Ding, Juntao Li, Pinzheng Wang, Zecheng Tang, Yan Bowen, Min Zhang

Information Extraction & Text Mining

Challenge:	Named Entity Recognition (NER) models are constrained by a pre-defined label set and require extensive human annotations, which limits their flexibility and adaptability to unseen tasks.
Approach:	They propose a Generative NER system that shows improved zero-shot performance across unseen entity domains by introducing contextual information and delineating label boundaries.
Outcome:	The proposed model outperforms state-of-the-art methods in zero-shot evaluation.

Enhancing the Open-Domain Dialogue Evaluation in Latent Space (2021.findings-acl)

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Zhangming Chan, Lemao Liu, Juntao Li, Haisong Zhang, Dongyan Zhao, Shuming Shi, Rui Yan

Dialogue Systems & Conversational Agents

Challenge:	Existing methods to evaluate opendomain dialogues are limited due to the one-to-many nature of dialogues.
Approach:	They propose a self-supervised setting to obtain a smooth latent space that captures discourse-level context information and implicitly models more references in latent spaces.
Outcome:	The proposed method outperforms baseline methods on two real-world dialogue datasets.

Tool learning via Inference-time Scaling and Cycle Verifier (2025.findings-acl)

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Xiaobo Liang, Wenjin Xie, Juntao Li, Wanfu Wang, Yibin Chen, Kehai Chen, Min Zhang

Challenge:	In inference-time scaling, Chain-of-Thought (CoT) data is scarce or even unavailable.
Approach:	They propose a method which establishes an inference cycle to synthesize user queries and CoT data.
Outcome:	The proposed method achieves a 75.4% pass rate and a 79.6% win rate using small models in StableToolBench.

Unlocking Recursive Thinking of LLMs: Alignment via Refinement (2025.findings-acl)

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Haoke Zhang, Xiaobo Liang, Cunxiang Wang, Juntao Li, Min Zhang

Challenge:	Existing methods for recursive reasoning are limited due to lack of expert-curated data.
Approach:	They propose a method that unlocks the potential of Large Language Models for recursive reasoning through long-form Chain of Thought.
Outcome:	The proposed method outperforms preference optimization methods on the openAI o1-series models by 20% on 3k synthetic samples.

When Slower Isn’t Truer: Inverse Scaling Law of Truthfulness in Multimodal Reasoning (2026.findings-acl)

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Sitong Fang, Wenjing Cao, Jiahao Li, Xuyao Wang, Chi-Min Chan, Sirui Han, Juntao Dai, Yike Guo, Yaodong Yang, Jiaming Ji

L-CiteEval: A Suite for Evaluating Fidelity of Long-context Models (2025.acl-long)

Challenge:	a study of slow reasoning models for multimodal reasoning finds that they are more prone to fabricating plausible yet false details when confronted with incomplete or misleading visual inputs.
Approach:	They conduct the first systematic study of the inverse scaling law in slow-thinking paradigms for multimodal reasoning.
Outcome:	The findings suggest that slower reasoning models are more prone to fabricating false details . the study analyzed 5,000-sample hierarchical prompt dataset by 50 participants .

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Zecheng Tang, Keyan Zhou, Juntao Li, Baibei Ji, Jianye Hou, Min Zhang

Challenge:	Long-context models (LCMs) have seen remarkable advancements in recent years, facilitating tasks like long-document QA.
Approach:	They propose an out-of-the-box suite that can assess both generation quality and fidelity in long-context understanding tasks.
Outcome:	The proposed suite can assess both generation quality and fidelity in long-context understanding tasks.

Decoder-Only LLMs can be Masked Auto-Encoders (2025.acl-short)

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Dan Qiao, Yuan Gao, Zheming Yang, Di Yang, Ziheng Wu, Pengcheng Lu, Minghui Qiu, Juntao Li, Min Zhang

Stick to the Facts: Learning towards a Fidelity-oriented E-Commerce Product Description Generation (D19-1)

Challenge:	Modern NLP workflows require different models for generation and embedding tasks.
Approach:	They propose a method that transforms an LLM into a Uni-Directional Masked Auto-Encoder.
Outcome:	The proposed method achieves state-of-the-art under unsupervised conditions with merely 100 training steps.

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Zhangming Chan, Xiuying Chen, Yongliang Wang, Juntao Li, Zhiqiang Zhang, Kun Gai, Dongyan Zhao, Rui Yan

Text Generation

Challenge:	Existing models for product description generation do not take the product attribute information into account.
Approach:	They propose a model that takes the embedding and the entity label of each word into account . they establish a keyword memory that stores the entity labels as keys and keywords as values .
Outcome:	The proposed model increases the fidelity of the generated descriptions by 25%.

Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training (2020.emnlp-main)

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Hai Ye, Qingyu Tan, Ruidan He, Juntao Li, Hwee Tou Ng, Lidong Bing

Isotropy-Enhanced Conditional Masked Language Models (2023.findings-emnlp)

Challenge:	Adapting pre-trained language models (PrLMs) to new domains has gained much attention . Adaptation of PrLMs to newdomains is important, but requires fine-tuning .
Approach:	They propose to use PrLMs to adapt to new domains without fine-tuning . they use class-aware feature self-distillation to learn discriminative features .
Outcome:	The proposed model can learn discriminative features from pre-trained language models without fine-tuning.

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Pei Guo, Yisheng Xiao, Juntao Li, Yixin Ji, Min Zhang

A Survey of Generative Information Extraction (2025.coling-main)

Challenge:	Existing non-autoregressive models with auto-regressing decoding paradigms have been used for various text generation tasks to accelerate inference but at the cost of generation quality to some extent.
Approach:	They propose to use Look Neighbors strategy to enhance learning of target token representations during training to achieve a good balance between inference speedup and generation quality.
Outcome:	The proposed models outperform current models on 4 WMT datasets and outperformed the current SoTA results.

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Zikang Zhang, Wangjie You, Tianci Wu, Xinrui Wang, Juntao Li, Min Zhang

G-SPEED: General SParse Efficient Editing MoDel (2023.findings-emnlp)

Challenge:	Information Extraction (IE) is a popular and fundamental task in natural language processing.
Approach:	They first review generative information extraction methods based on pre-trained language models and large language models focusing on their adaptation and generalization capabilities.
Outcome:	The proposed methods are based on pre-trained language models and large language models, and emphasize the importance of model collaboration.

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Haoke Zhang, Yue Wang, Juntao Li, Xiabing Zhou, Min Zhang

Robust Question Answering against Distribution Shifts with Test-Time Adaption: An Empirical Study (2022.findings-emnlp)

Challenge:	Large Language Models (LLMs) have demonstrated incredible capabilities in understanding, generating, and manipulating languages.
Approach:	They propose a general SParse Efficient Editing MoDel which can fulfill diverse editing requirements through a single model while maintaining low computational costs.
Outcome:	The proposed model can fulfill diverse editing requirements through a single model while maintaining low computational costs.

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Hai Ye, Yuyang Ding, Juntao Li, Hwee Tou Ng

Robustness in NLP

Challenge:	Existing work on robustness tuning (RT) methods has found that QA models fail when the test data has a distribution shift compared to the training data.
Approach:	They propose to use test-time adaptation methods to improve QA models after deployment to evaluate their model against text corruption and changes in language and domain.
Outcome:	The proposed method improves TTA to be more robust to variation in hyper-parameters and test distributions over time.

Escaping the Echo Trap: On Credit Assignment Failure in Multi-turn LLM Self-Reflection (2026.acl-long)

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Linxuan Du, Guangquan Xue, Xiaobo Liang, Qipeng Huang, Yuyang Ding, Xinyu Shi, Zhang Yijun, Ji Qi, Wenpeng Zhu, Juntao Li, Min Zhang

Challenge:	Existing methods for multi-turn self-reflection are limited by the Echo Trap problem . the model is limited by its inherent capabilities and repeats earlier reflections to preserve reward signals .
Approach:	They propose a tree-structured extension of GRPO for multi-turn self-reflection which enables more accurate advantage estimation.
Outcome:	The proposed method mitigates behavior collapse and improves performance across benchmarks.

From Awareness to Adaptability: Enhancing Tool Utilization for Scientific Reasoning (2025.findings-acl)

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Wenjing Xie, Xiaobo Liang, Juntao Li, Wanfu Wang, Kehai Chen, Qiaoming Zhu, Min Zhang

BatonVoice: An Operationalist Framework for Enhancing Controllable Speech Synthesis with Linguistic Intelligence from LLMs (2026.acl-long)

Challenge:	Existing approaches enhance reasoning through Chain-of-Thought, Program-ofThough, and Tool-Integration.
Approach:	They propose a tool-awareness training method that leverages both forward and backward data generation strategies to strengthen the model’s conscious and selective tool utilization in multi-step reasoning tasks.
Outcome:	The proposed method improves the model's tool utilization capabilities, including proactivity and execution success rates.

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Yue Wang, Ruotian Ma, Xingyu Chen, Zhengliang Shi, Morunliu Yang, Wanshun Chen, Huang Liu, Jiadi Yao, Xin He, Qu Yang, Qingxuan Jiang, Fanghua Ye, Juntao Li, Zhaopeng Tu, Xiaolong Li, Liefeng Bo, Min Zhang

Speech & Audio in NLP

Challenge:	Existing approaches often fail to leverage the linguistic intelligence of Large Language Models (LLMs) Existing models lack the ability to follow text instructions for controllable Text-to-Speech (TTS).
Approach:	They propose a framework where an LLM acts as a conductor, understanding user instructions and generating a textual plan - explicit vocal features.
Outcome:	The proposed model outperforms open- and closed-source models in speech synthesis and achieves zero-shot cross-lingual generalization.

Exploring and Mitigating Shortcut Learning for Generative Large Language Models (2024.lrec-main)

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Zechen Sun, Yisheng Xiao, Juntao Li, Yixin Ji, Wenliang Chen, Min Zhang

Open-ended Long Text Generation via Masked Language Modeling (2023.acl-long)

Challenge:	Recent large language models (LLMs) have incredible instruction-following capabilities while maintaining strong task completion ability.
Approach:	They propose a framework to encourage LLMs to Forget Spurious correlations and Learn from In-context information.
Outcome:	The proposed framework can mitigate shortcut learning by forging spurious correlations and learning from in-context information.

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Xiaobo Liang, Zecheng Tang, Juntao Li, Min Zhang