Papers by Jiajun Zhang

Muse: Towards Reproducible Long-Form Song Generation with Fine-Grained Style Control (2026.findings-acl)

Challenge:	Large Language Models (LLMs) have shown impressive language capabilities, but most of them have very unbalanced performance across different languages.
Approach:	They propose to use question translation data to enhance LLMs' multilingual capabilities by using mechanistic interpretability methods.
Outcome:	The proposed method improves multilingual alignment even with unannotated answers in English and a wide range of languages even with instruction-tuned LLMs.

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Changhao Jiang, Jiahao Chen, Zhenghao Xiang, Zhixiong Yang, Hanchen Wang, Jiabao Zhuang, Xinmeng Che, Jiajun Sun, Hui Li, Yifei Cao, Shihan Dou, Ming Zhang, Junjie Ye, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

Speech & Audio in NLP

Challenge:	Recent commercial systems such as Suno demonstrate strong capabilities in long-form song generation, but academic research remains non-reproducible due to the lack of publicly available training data.
Approach:	They propose a system for long-form song generation with fine-grained style conditioning that includes a licensed synthetic dataset and a song generation model, Muse.
Outcome:	The proposed system achieves competitive performance on phoneme error rate, text–music style similarity, and audio aesthetic quality while enabling controllable segment-level generation across different musical structures.

DentalGPT: Incentivizing Multimodal Reasoning in Dentistry (2026.findings-acl)

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Zhenyang Cai, Jiaming Zhang, Junjie Zhao, Ziyi Zeng, Yanchao Li, Liang Jingyi, Junying Chen, Yunjin Yang, Jiajun You, Shuzhi Deng, null Xieruiqiii, Yuanting Chen, Xiangyi Feng, Jianquan Li, Liangyi Chen, Junwen Wang, Shan Jiang, Benyou Wang

Exploiting Curriculum Learning in Unsupervised Neural Machine Translation (2021.findings-emnlp)

Challenge:	Current multimodal large language models (MLLMs) show limited understanding of dental images.
Approach:	They propose a dental-specialized multimodal large language model trained via staged multimodal alignment and reinforcement learning.
Outcome:	The proposed model outperforms state-of-the-art models on disease classification and dental VQA tasks.

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Jinliang Lu, Jiajun Zhang

Norm-based Noisy Corpora Filtering and Refurbishing in Neural Machine Translation (2022.emnlp-main)

Challenge:	Experimental results show that the proposed method achieves consistent improvements with faster convergence speed.
Approach:	They propose a curriculum learning method to gradually utilize pseudo bi-texts based on their quality from multiple granularities.
Outcome:	The proposed method achieves consistent improvements with faster convergence speed on WMT 14 En-Fr, WMT14 En-De, and LDC En-Zh translation tasks.

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Yu Lu, Jiajun Zhang

Addressing Troublesome Words in Neural Machine Translation (D18-1)

Challenge:	Existing noisy corpora filtering methods are insufficient to solve this problem, requiring multiple scorers trained on clean bitexts.
Approach:	They propose to use the information ratio from the source to the target side to distinguish unparallel sentence pairs by using norms of context vectors.
Outcome:	The proposed method performs comparably with state-of-the-art noisy corpora filtering techniques but is more efficient and easier to operate.

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Yang Zhao, Jiajun Zhang, Zhongjun He, Chengqing Zong, Hua Wu

Memory Consolidation for Contextual Spoken Language Understanding with Dialogue Logistic Inference (P19-1)

Challenge:	Neural machine translation (NMT) has weaknesses in handling lowfrequency and ambiguous words, which we refer to as troublesome words.
Approach:	They propose to use contextual memory to memorize which target words should be produced in which situations to translate troublesome words.
Outcome:	The proposed method outperforms baseline models on Chinese-to-English and English-to German translation tasks.

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He Bai, Yu Zhou, Jiajun Zhang, Chengqing Zong

Dialogue Systems & Conversational Agents

Challenge:	Existing models for SLU use explicit memory representations, but the context memory is under-exploited.
Approach:	They propose a dialogue logistic inference task to consolidate the context memory with SLU in a multi-task framework.
Outcome:	The proposed model improves slot filling and domain classification performance in a multi-task framework.

AgentGym2: Benchmarking Large Language Model Agents in De-Idealized Real-World Environments (2026.acl-long)

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Zhiheng Xi, Dingwen Yang, Jiaqi Liu, Jixuan Huang, Honglin Guo, Baodai Huang, Tinggang Chen, Qi Zhang, Zhonghang Lu, Chenyu Liu, Jiajun Sun, Jiazheng Zhang, Dingwei Zhu, Xin Guo, Junzhe Wang, Zhihao Zhang, Yuming Yang, Junjie Ye, Minghe Gao, Dongrui Liu, Jiaming Ji, Guohao Li, Tao Gui, Qi Zhang, Xuanjing Huang

IMTLab: An Open-Source Platform for Building, Evaluating, and Diagnosing Interactive Machine Translation Systems (2023.emnlp-main)

Challenge:	Existing benchmarks evaluate agents in simplified, idealized settings, relying on pre-packaged tool interfaces, overlooking critical steps, and assume inputs are clean and fully specified.
Approach:	They propose a framework that evaluates language agents in simplified, idealized settings . they show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .
Outcome:	Experiments on 15 proprietary and open-source models show that even SOTA systems like Gemini and GPT-5 struggle on AgentGym2 .

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Xu Huang, Zhirui Zhang, Ruize Gao, Yichao Du, Lemao Liu, Guoping Huang, Shuming Shi, Jiajun Chen, Shujian Huang

LoopCoder: Scaling Code Intelligence via Looped Language Models (2026.findings-acl)

Challenge:	Existing systems that use a left-to-right completion paradigm are inefficient and expensive.
Approach:	They propose an open-source end-to-end interactive machine translation system platform . they propose to use a prefix-constrained decoding approach to achieve end- to-end evaluation .
Outcome:	The proposed system can guarantee high-quality, error-free translations . it uses prefix-constrained decoding and improves on previous systems .

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Jian Yang, Wei Zhang, Shuyue Guo, Yizhi LI, Linzheng Chai, Zhengmao Ye, Shukai Liu, Yuyang Song, Jiajun Wu, Che Liu, Tianyu Zheng, Siwei Wu, Leo L, Xudong Ma, Chuan Hao, Ran Tao, Yan Xing, Jianzhou Wang, Mingjie Tang, Aishan Liu, Zhoujun Li, Xianglong Liu, Weifeng Lv, Bryan Dai

Data Whisperer: Efficient Data Selection for Task-Specific LLM Fine-Tuning via Few-Shot In-Context Learning (2025.acl-long)

Challenge:	Large language models have mastered syntax-level code generation, but complex algorithmic reasoning remains a challenge.
Approach:	They propose a recurrent inductive bias that aligns with the recursive nature of programming logic.
Outcome:	The proposed model achieves comparable performance to standard dense models with more parameters.

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Shaobo Wang, Xiangqi Jin, Ziming Wang, Jize Wang, Jiajun Zhang, Kaixin Li, Zichen Wen, Zhong Li, Conghui He, Xuming Hu, Linfeng Zhang

Responsible & Trustworthy NLP

Challenge:	Using fine-tuning on task-specific data is essential for large language models to be effective in specialized tasks.
Approach:	They propose a method that leverages few-shot in-context learning with the model to be fine-tuned.
Outcome:	The proposed method outperforms existing methods with a 3.1-point improvement and a 7.4 speedup on the Llama-3-8B-Instruct model using just 10% of the dataset.

X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions (2024.findings-acl)

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Chong Li, Wen Yang, Jiajun Zhang, Jinliang Lu, Shaonan Wang, Chengqing Zong

Addressing Asymmetry in Multilingual Neural Machine Translation with Fuzzy Task Clustering (2022.coling-1)

Challenge:	Large language models respond well in high-resource languages but struggle in low-resourced languages.
Approach:	They propose a method to construct cross-lingual instruction following samples with instruction in English and response in low-resource languages.
Outcome:	The proposed method builds a large-scale cross-lingual instruction tuning dataset on 10 languages.

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Qian Wang, Jiajun Zhang

Cross-Modal Cloze Task: A New Task to Brain-to-Word Decoding (2022.findings-acl)

Challenge:	Existing clustering methods cannot handle asymmetric problem in multilingual NMT . existing models cannot handle the asymmetry problem since there are thousands of languages involved .
Approach:	They propose a fuzzy task clustering method to address the asymmetric problem in multilingual NMT by using task affinity as the clustering criterion.
Outcome:	The proposed method outperforms baselines for a multilingual model and the existing models.

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Shuxian Zou, Shaonan Wang, Jiajun Zhang, Chengqing Zong

Text Classification

Challenge:	Existing work on decoding language from non-invasive brain activity is limited due to noisy nature of brain recordings.
Approach:	They propose a cross-modal cloze task to predict a word from a neural image . they use a pre-trained language model to leverage the pre-training language model .
Outcome:	The proposed method outperforms baselines on 20 participants from two brain imaging datasets.

A Compact and Language-Sensitive Multilingual Translation Method (P19-1)

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Yining Wang, Long Zhou, Jiajun Zhang, Feifei Zhai, Jingfang Xu, Chengqing Zong

RealChart2Code: Bridging the Gap in Real-World Chart-to-Code Generation via Multi-Task Evaluation (2026.acl-long)

Challenge:	Existing paradigms for multilingual neural machine translation do not make full use of language commonality and parameter sharing.
Approach:	They propose a multilingual neural machine translation paradigm with one encoder-decoder model that makes full use of language commonality and parameter sharing.
Outcome:	The proposed method outperforms strong standard multilingual translation systems on WMT and IWSLT datasets.

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Jiajun Zhang, Yuying Li, Zhixun Li, Xingyu Guo, Jingzhuo Wu, Leqi Zheng, Yiran Yang, Jianke Zhang, Qingbin Li, Shannan Yan, Changguo Jia, Junfei Wu, Zilei Wang, Qiang Liu, Liang Wang

Teaching Vision-Language Models to Ask: Resolving Ambiguity in Visual Questions (2025.acl-long)

Challenge:	Vision-Language Models (VLMs) have demonstrated impressive capabilities in code generation across various domains, but their ability to replicate complex, multi-panel visualizations remains largely unassessed.
Approach:	They propose a large-scale benchmark to evaluate chart generation from large- scale raw data and assess iterative code refinement in a multi-turn conversational setting.
Outcome:	The new benchmark evaluates 14 leading VLMs on real-world data and shows they struggle with complex plot structures and authentic data.

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Pu Jian, Donglei Yu, Wen Yang, Shuo Ren, Jiajun Zhang

Parameter-efficient Tuning for Large Language Model without Calculating Its Gradients (2023.emnlp-main)

Challenge:	Existing research addresses ambiguous visual questions by rephrasing questions, but it fails to address the inherently interactive nature of user interactions with visual language models (VLMs). Existing studies focus on re-phrase questions, and lack of a benchmark to assess VLMs’ capacity for resolving ambiguities through interaction.
Approach:	They propose a visual question answering task that provides a natural language answer to a question based on a given image and an automated pipeline to generate ambiguity-clarification question pairs.
Outcome:	The proposed benchmark targets three common categories of ambiguity in visual question answering (VQA) context and encompasses various VQA scenarios.

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Feihu Jin, Jiajun Zhang, Chengqing Zong

SceMQA: A Scientific College Entrance Level Multimodal Question Answering Benchmark (2024.acl-short)

Challenge:	Recent parameter-efficient tuning methods can only save 30% of training memory . gradient computation and backpropagation are still necessary for these methods .
Approach:	They propose a parameter-efficient tuning method that can be used to fine-tune large language models without calculating gradients.
Outcome:	The proposed method saves 30% of training memory and improves performance on large language models.

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Zhenwen Liang, Kehan Guo, Gang Liu, Taicheng Guo, Yujun Zhou, Tianyu Yang, Jiajun Jiao, Renjie Pi, Jipeng Zhang, Xiangliang Zhang

QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models (2025.emnlp-main)

Challenge:	SceMQA focuses on core science subjects including Mathematics, Physics, Chemistry, and Biology.
Approach:	They propose to use SceMQA to evaluate multimodal question answering at college entrance level.
Outcome:	The proposed model provides specific knowledge points for each problem and detailed explanations for each answer.

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Jiajun Zhou, Yifan Yang, Kai Zhen, Ziyue Liu, Yequan Zhao, Ershad Banijamali, Athanasios Mouchtaris, Ngai Wong, Zheng Zhang

An Efficient and Precise Training Data Construction Framework for Process-supervised Reward Model in Mathematical Reasoning (2025.acl-long)

Challenge:	Large Language Models (LLMs) are quantized to lower precision to reduce memory cost and latency in inference.
Approach:	They propose a quantized zeroth-order framework for fine-tuning Large Language Models (LLMs) using low-precision forward passes.
Outcome:	The proposed method achieves comparable results to first-order methods in FP8 and superior accuracy in INT8 and INT4 training.

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Wei Sun, Qianlong Du, Fuwei Cui, Jiajun Zhang

Adaptive Nearest Neighbor Machine Translation (2021.acl-short)

Challenge:	Existing methods for constructing process supervision training data are costly or suffer from poor quality.
Approach:	They propose a framework called EpicPRM which annotates each intermediate reasoning step based on its quantified contribution and uses an adaptive binary search algorithm to enhance annotation precision and efficiency.
Outcome:	The proposed framework improves annotation precision and efficiency and can be used to train a high-quality training dataset with 50k annotated intermediate steps.

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Xin Zheng, Zhirui Zhang, Junliang Guo, Shujian Huang, Boxing Chen, Weihua Luo, Jiajun Chen

Enhancing Knowledge Distillation of Large Language Models through Efficient Multi-Modal Distribution Alignment (2025.coling-main)

Challenge:	kNN-MT uses pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy.
Approach:	They propose a method that combines a pre-trained NMT model with token-level k-nearest-neighbor retrieval to improve translation accuracy.
Outcome:	The proposed method outperforms the existing model on four benchmark datasets and is open-source.

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Tianyu Peng, Jiajun Zhang

TokAlign: Efficient Vocabulary Adaptation via Token Alignment (2025.acl-long)

Challenge:	Existing knowledge distillation techniques for large language models are causing difficulties for student models to learn multi-modal probability distributions.
Approach:	They propose a ranking loss-based knowledge distillation method that encourages consistency of the ranking of peak predictions between teacher and student models.
Outcome:	The proposed method improves student models' ability to learn multi-modal distributions.

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Chong Li, Jiajun Zhang, Chengqing Zong

Green & Sustainable NLP

Challenge:	Tokenization is a foundational step for Large Language Models (LLMs) but low compression rate of vanilla tokenizers decelerates training and inference process.
Approach:	They propose a method to replace the vocabulary of Large Language Models (LLMs) by learning a one-to-one mapping matrix for token IDs.
Outcome:	The proposed method significantly improves multilingual text compression rates and vocabulary initialization for Large Language Models.

Are You for Real? Detecting Identity Fraud via Dialogue Interactions (D19-1)

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Weikang Wang, Jiajun Zhang, Qian Li, Chengqing Zong, Zhifei Li

Knowledge Graph Enhanced Neural Machine Translation via Multi-task Learning on Sub-entity Granularity (2020.coling-main)

Challenge:	Existing methods to detect identity fraud are prone to errors and are not based on real data.
Approach:	They propose to use a KG constructor and structured dialogue management to detect identity fraud in loan applications to generate questions based on personal information.
Outcome:	The proposed system can detect fraudsters and achieve higher recognition accuracy compared with rule-based systems.

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Yang Zhao, Lu Xiang, Junnan Zhu, Jiajun Zhang, Yu Zhou, Chengqing Zong

LADM: Long-context Training Data Selection with Attention-based Dependency Measurement for LLMs (2025.acl-long)

Challenge:	Existing methods to integrate knowledge graph (KG) with neural machine translation (NMT) have two problems: knowledge under-utilization and granularity mismatch.
Approach:	They propose a multi-task learning method on sub-entity granularity to combine machine translation and knowledge reasoning tasks.
Outcome:	The proposed method significantly outperforms baseline models on translation tasks and handling the entities.

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Jianghao Chen, Junhong Wu, Yangyifan Xu, Jiajun Zhang

Challenge:	Long-context modeling has drawn more attention in the area of Large Language Models (LLMs).
Approach:	They propose a Long-context data selection framework with Attention-based Dependency Measurement which can efficiently identify high-quality long-contrast data from a large-scale, multi-domain pre-training corpus.
Outcome:	The proposed framework significantly boosts the performance of LLMs on multiple long-context tasks with only 1B tokens for continual training.

Attend, Translate and Summarize: An Efficient Method for Neural Cross-Lingual Summarization (2020.acl-main)

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Junnan Zhu, Yu Zhou, Jiajun Zhang, Chengqing Zong

Incremental Learning from Scratch for Task-Oriented Dialogue Systems (P19-1)

Challenge:	Existing methods for cross-lingual summarization are pipeline-based, but they suffer from error propagation.
Approach:	They propose a method that attends to some words in the source text, then translates them into the target language to get the final summary.
Outcome:	The proposed method outperforms baseline methods on Chinese-to-English and English-to Chinese summarization tasks.

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Weikang Wang, Jiajun Zhang, Qian Li, Mei-Yuh Hwang, Chengqing Zong, Zhifei Li

MARCH: Multi-Agent Reinforced Check for Hallucination (2026.acl-long)

Challenge:	Existing task-oriented dialogue systems cannot guarantee that all user needs are taken into account in the design phase.
Approach:	They propose a new incremental learning framework to design task-oriented dialogue systems without pre-defining user needs.
Outcome:	The proposed framework is robust to unconsidered user actions and can update itself online with less annotation cost.

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Zhuo Li, Yupeng Zhang, Pengyu Cheng, Jiajun Song, Mengyu Zhou, Hao Li, Shujie Hu, Yu Qin, null Erchao.zec, Xiaoxi Jiang, null Guanjunjiang

Touch Editing: A Flexible One-Time Interaction Approach for Translation (2020.aacl-main)

Challenge:	Existing methods to detect hallucinations suffer from inherent confirmation bias, where the verifier inadvertently reproduces the errors of the original generation.
Approach:	They propose a framework that enforces rigorous factual alignment by leveraging deliberate information asymmetry by combining a pipeline of three specialized agents: a Solver, a Proposer, and a Checker.
Outcome:	Extensive experiments across hallucination benchmarks demonstrate that MARCH substantially reduces hallucinism rates.

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Qian Wang, Jiajun Zhang, Lemao Liu, Guoping Huang, Chengqing Zong

One Sentence One Model for Neural Machine Translation (L18-1)

Challenge:	Existing methods for machine translation require intensive keyboard interaction, which is inconvenient on mobile devices.
Approach:	They propose a touch-based editing method that is more flexible than keyboard-mouse-based translation postediting.
Outcome:	The proposed method significantly outperforms existing interactive translation methods on translation datasets and on post-editing datasets.

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Xiaoqing Li, Jiajun Zhang, Chengqing Zong

Lost in the Source Language: How Large Language Models Evaluate the Quality of Machine Translation (2024.findings-acl)

Challenge:	Neural machine translation (NMT) is a new state of the art that can produce better results than traditional statistical machine translation.
Approach:	They propose a dynamic neural network which learns a general network as usual and fine-tunes it for each test sentence.
Outcome:	The proposed method improves translation performance when similar sentences are available.

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Xu Huang, Zhirui Zhang, Xiang Geng, Yichao Du, Jiajun Chen, Shujian Huang

MixRED: A Mix-lingual Relation Extraction Dataset (2024.lrec-main)

Challenge:	Recent studies have shown that Large Language Models (LLMs) can be used as translation evaluators.
Approach:	They propose to use both coarse-grained and fine-grounded prompts to discern the utility of source versus reference data in machine translation evaluation tasks.
Outcome:	The proposed model can be used to evaluate translations in multiple languages.

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Lingxing Kong, Yougang Chu, Zheng Ma, Jianbing Zhang, Liang He, Jiajun Chen

Information Extraction & Text Mining

Challenge:	Existing research focuses on monolingual relation extraction, but there is a significant gap in understanding relation extraction in the mix-lingual scenario.
Approach:	They propose a task of considering relation extraction in the mix-lingual scenario . they construct a human-annotated dataset to support the task .
Outcome:	The proposed task evaluates state-of-the-art supervised models and large language models on the human-annotated dataset MixRED.

Group then Scale: Dynamic Mixture-of-Experts Multilingual Language Model (2025.findings-acl)

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Chong Li, Yingzhuo Deng, Jiajun Zhang, Chengqing Zong

CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models (2024.findings-acl)

Challenge:	Experimental results show that language group specialization on experts improves multilingual performance.
Approach:	They propose to dynamically group and scale up parameters of multilingual Large Language Models while boosting positive transfer among similar languages.
Outcome:	The proposed method reduces negative transfer between languages and boosts performance on 18 to 128 languages.

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Yizhi Li, Ge Zhang, Xingwei Qu, Jiali Li, Zhaoqun Li, Noah Wang, Hao Li, Ruibin Yuan, Yinghao Ma, Kai Zhang, Wangchunshu Zhou, Yiming Liang, Lei Zhang, Lei Ma, Jiajun Zhang, Zuowen Li, Wenhao Huang, Chenghua Lin, Jie Fu

Improved Pseudo Data for Machine Translation Quality Estimation with Constrained Beam Search (2023.emnlp-main)

Challenge:	a recent study shows that large language models have limited generalization in low-resource languages like Chinese.
Approach:	They propose to evaluate the zero-shot generalizability of large language models to the Chinese language . they release only half of the dataset publicly, with the remainder kept private .
Outcome:	The Chinese Instruction-Following Benchmark evaluates the generalizability of LLMs to the Chinese language.

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Xiang Geng, Yu Zhang, Zhejian Lai, Shuaijie She, Wei Zou, Shimin Tao, Hao Yang, Jiajun Chen, Shujian Huang

EvoHyper: Evolving Hypergraph Topologies for Unified Collaboration in Multi-Agent Communication (2026.findings-acl)

Challenge:	evaluating the quality of machine translation outputs becomes increasingly essential with the rapid development of machine language (MT).
Approach:	They propose to generate pseudo data using the MT model with constrained beam search (CBSQE) they propose to preserve the reference parts with high MT probabilities as correct translations .
Outcome:	The proposed model outperforms strong baselines in both supervised and unsupervised settings.

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Heng Zhang, Yihao Zhong, Lubin Gan, Zhihe Chen, Jiajun Wu, Yuling Shi, Xiaodong Gu, Hao Zhang, Haochen You, Jin Huang

Challenge:	Existing methods for multi-agent collaboration use a fixed communication graph and manage collaboration structure and shared memory in separate modules.
Approach:	They propose a framework that uses an evolving hypergraph topology for multi-agent collaboration.
Outcome:	The proposed framework achieves 3.2% to 7.8% accuracy gains over state-of-the-art methods and efficient, reducing token consumption by up to 23.5%.

FGraDA: A Dataset and Benchmark for Fine-Grained Domain Adaptation in Machine Translation (2022.lrec-1)

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Wenhao Zhu, Shujian Huang, Tong Pu, Pingxuan Huang, Xu Zhang, Jian Yu, Wei Chen, Yanfeng Wang, Jiajun Chen

OpenS2S: Advancing Fully Open-Source End-to-End Empathetic Large Speech Language Model (2025.emnlp-demos)

Challenge:	Recent research on domain adaptation neglects diversity in translation within a domain . current research on NMT models considers very broad target domains .
Approach:	They propose a fine-grained domain adaptation task for autonomous vehicles, AI education, real-time networks, and smart phone.
Outcome:	The proposed task is compared with a dataset of Chinese-English translation tasks for four sub-domains of information technology: autonomous vehicles, AI education, real-time networks, and smart phone.

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Chen Wang, Tianyu Peng, Wen Yang, YiNan Bai, Guangfu Wang, Jun Lin, Lanpeng Jia, Lingxiang Wu, Jinqiao Wang, Chengqing Zong, Jiajun Zhang

Distill and Replay for Continual Language Learning (2020.coling-main)

Challenge:	Empathetic speech models are increasingly closed off, leaving details about the architecture, data and development opaque to researchers.
Approach:	They propose an open-source empathetic speech-to-text model with a streaming interleaved decoding architecture and a data pipeline to enable end-to end training.
Outcome:	The proposed model is open-source and transparent, with no data or data required to build it.

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Jingyuan Sun, Shaonan Wang, Jiajun Zhang, Chengqing Zong

MdEval: Massively Multilingual Code Debugging (2026.findings-acl)

Challenge:	Existing models fail to isolate acquired knowledge and forget previously learned tasks when learning in a stream where data distribution may shift.
Approach:	They propose a framework that distills knowledge and replays experience from previous tasks when fitting on a new task.
Outcome:	The proposed framework outperforms state-of-the-art models in continuously learning tasks of the same type but from different domains, as well as tasks of different types.

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Shukai Liu, Linzheng Chai, Jian Yang, Jiajun Shi, He Zhu, Liran Wang, Jin Ke, Wei Zhang, Hualei Zhu, Shuyue Guo, Tao Sun, Jiaheng Liu, Yunlong Duan, Yu Hao, Liqun Yang, Guanglin Niu, Ge Zhang, Zhoujun Li

UIOrchestra: Generating High-Fidelity Code from UI Designs with a Multi-agent System (2025.findings-emnlp)

Challenge:	Existing benchmarks primarily focus on Python and are limited in terms of language diversity.
Approach:	They propose a multilingual debugging benchmark that includes 3.9K test samples of 20 programming languages and introduces the debug instruction corpora MdEval-Instruct by injecting bugs into the correct multilingual queries and solutions.
Outcome:	The proposed benchmark includes 3.9K test samples of 20 programming languages and covers the automated program repair task, bug localization task, and bug identification task.

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Chuhuai Yue, Jiajun Chai, Yufei Zhang, Zixiang Ding, Xihao Liang, Peixin Wang, Shihai Chen, Wang Yixuan, null Wangyanping, Guojun Yin, Wei Lin

Exploiting Pre-Ordering for Neural Machine Translation (L18-1)

Challenge:	Recent advances in large language models have significantly improved automated code generation . however, the translation of complex mobile UI designs into high-fidelity front-end code remains a challenge .
Approach:	They propose a collaborative multi-agent system to reconstruct static single-page apps from mockups.
Outcome:	The proposed system outperforms existing methods in reconstructing complex app pages . the code and data will be released upon paper acceptance .

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Yang Zhao, Jiajun Zhang, Chengqing Zong

Take a Closer Look at Multilinguality! Improve Multilingual Pre-Training Using Monolingual Corpora Only (2023.findings-emnlp)

Challenge:	Existing studies have shown that Neural Machine Translation suffers from the problems that some source words are mistakenly translated for multiple times .
Approach:	They propose a pre-ordering approach to solve the under-translation problem by pre-ordnanced source sentences and position embedding to enhance monotone translation.
Outcome:	The proposed method significantly improves translation quality by 2.43 BLEU points on Chinese-to-English translation.

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Jinliang Lu, Yu Lu, Jiajun Zhang

Entity-level Cross-modal Learning Improves Multi-modal Machine Translation (2021.findings-emnlp)

Challenge:	Recent studies have demonstrated remarkable cross-lingual capability of pre-trained language models . however, semantic alignments may be the reason behind such capability but remain under-explored.
Approach:	They propose token-level and semantic-level code-switched masked language modeling to improve cross-lingual interactions over mono-mPLMs without parallel sentences.
Outcome:	The proposed method outperforms mono-mPLMs on natural language understanding and unsupervised machine translation tasks.

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Xin Huang, Jiajun Zhang, Chengqing Zong

CapArena: Benchmarking and Analyzing Detailed Image Captioning in the LLM Era (2025.findings-acl)

Challenge:	Multi-modal machine translation aims at improving translation performance by incorporating visual information.
Approach:	They propose an explicit entity-level cross-modal learning approach that aims to augment the entity representation by combining a translation task and a reconstruction task.
Outcome:	The proposed approach achieves comparable or even better performance than state-of-the-art models.

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Kanzhi Cheng, Wenpo Song, Jiaxin Fan, Zheng Ma, Qiushi Sun, Fangzhi Xu, Chenyang Yan, Nuo Chen, Jianbing Zhang, Jiajun Chen

Local Interpretation of Transformer Based on Linear Decomposition (2023.acl-long)

Challenge:	Image captioning has been a challenge for vision-language researchers for decades . current VLMs focus on tasks like visual question answering (YA) but image captioning is not as advanced as expected.
Approach:	They evaluate VLMs' performance on image captioning using human annotations . they find that some metrics show high caption-level agreement with humans .
Outcome:	The proposed model outperforms open-source models on image captioning . it achieves 93.4% correlation with human rankings at $4 per test .

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Sen Yang, Shujian Huang, Wei Zou, Jianbing Zhang, Xinyu Dai, Jiajun Chen

Explainability & Interpretability in NLP

Challenge:	Existing work on local explanation generation attempts to understand model dynamics on word-level or phraselevel by assigning importance scores on input features.
Approach:	They propose to interpret neural networks by linear decomposition by a Transformer model on a single input and a linear decomposing of the output to generate local explanations.
Outcome:	The proposed method achieves competitive performance in sentiment classification and machine translation, and fidelity of explanation.

Learning Confidence for Transformer-based Neural Machine Translation (2022.acl-long)

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Yu Lu, Jiali Zeng, Jiajun Zhang, Shuangzhi Wu, Mu Li

A Teacher-Student Framework for Maintainable Dialog Manager (D18-1)

Challenge:	A well-calibrated confidence estimate is not sufficient for neural machine translation (NMT) where probabilities from softmax distribution fail to describe when the model is probably mistaken.
Approach:	They propose an unsupervised confidence estimate learning jointly with the training of a neural machine translation model to quantify confidence.
Outcome:	The proposed model outperforms standard label smoothing and can predict failures in two real-world scenarios.

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Weikang Wang, Jiajun Zhang, Han Zhang, Mei-Yuh Hwang, Chengqing Zong, Zhifei Li

AutoSearch: Adaptive Search Depth for Efficient Agentic RAG via Reinforcement Learning (2026.findings-acl)

Challenge:	Reinforcement learning (RL) is an attractive solution for task-oriented dialog systems . but extending RL-based systems to handle new intents and slots requires a system redesign .
Approach:	They propose a teacher-student framework to extend RL-based dialog systems . they propose to specify constraints held in the new dialog manager .
Outcome:	The proposed framework makes no assumption about unsupported intents and slots, making it possible to improve RL-based systems incrementally.

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Jingbo Sun, Wenyue Chong, Songjun Tu, Qichao Zhang, Yaocheng Zhang, Jiajun Chai, Xiaohan Wang, Wei Lin, Guojun Yin, Dongbin Zhao

Green & Sustainable NLP

Challenge:	Prior work limits search depth to reduce cost, but this often leads to underexploration of complex questions.
Approach:	They propose a reinforcement learning framework that evaluates each search step via self-generated intermediate answers.
Outcome:	Extensive experiments on multiple benchmarks show that AutoSearch achieves a superior accuracy-efficiency trade-off, alleviating over-searching while preserving search quality.

Collaborative Beam Search: Enhancing LLM Reasoning via Collective Consensus (2025.emnlp-main)

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Yangyifan Xu, Shuo Ren, Jiajun Zhang

Discrete Cross-Modal Alignment Enables Zero-Shot Speech Translation (2022.emnlp-main)

Challenge:	Existing approaches to improve the reasoning capabilities of large language models (LLMs) depend on domain-specific external verifiers or self-evaluation which is brittle and prompt-sensitive.
Approach:	They propose a framework that harnesses the collective intelligence of multiple large language models across both generation and verification stages.
Outcome:	The proposed framework outperforms singlemodel scaling and multi-model ensemble baselines on six tasks by over 4 percentage points in average accuracy.

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Chen Wang, Yuchen Liu, Boxing Chen, Jiajun Zhang, Wei Luo, Zhongqiang Huang, Chengqing Zong

V-GameGym: Visual Game Generation for Code Large Language Models (2026.findings-acl)

Challenge:	Existing zero-shot methods fail to align speech and text into a shared semantic space . Existing methods require expensive and expensive parallel ST data .
Approach:	They propose a method that uses a shared discrete vocabulary space to align speech and text into a common space.
Outcome:	The proposed method significantly improves the SOTA and even performs on par with the strong supervised ST baselines.

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Wei Zhang, Jian Yang, Renshuai Tao, Linzheng Chai, Shuyue Guo, Jiajun Wu, Xiaoming Chen, Ganqu Cui, Ning Ding, Xander Xu, HU Wei, Bowen Zhou

BLSP-Emo: Towards Empathetic Large Speech-Language Models (2024.emnlp-main)

Challenge:	Existing code-related benchmarks focus on single modality rather than visual game development.
Approach:	They propose a multimodal benchmark for evaluating code large language models in visual game generation that integrates a clustering-based curation methodology and a pipeline for visual code synthesis.
Outcome:	The proposed framework assesses code generation and visual game generation using a sandbox environment.

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Chen Wang, Minpeng Liao, Zhongqiang Huang, Junhong Wu, Chengqing Zong, Jiajun Zhang

Long-form Hallucination Detection with Self-elicitation (2025.findings-acl)

Challenge:	BLSP-Emo model understands both semantics and emotions in speech and generates empathetic responses.
Approach:	They propose a language-speech pretraining with emotion support that utilizes existing speech and emotion recognition datasets to create an end-to-end speech-language model.
Outcome:	The proposed model can understand both semantics and emotions in speech and generate empathetic responses.

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Zihang Liu, Jiawei Guo, Hao Zhang, Hongyang Chen, Jiajun Bu, Haishuai Wang

Training Simultaneous Speech Translation with Robust and Random Wait-k-Tokens Strategy (2023.emnlp-main)

Challenge:	Existing methods for hallucination detection tend to decompose text into isolated statements, unable to understand contextual semantics.
Approach:	They propose a framework to leverage self-generated thoughts derived from prior statements as catalysts to elicit the expression of intrinsic knowledge and understand contextual semantics.
Outcome:	The proposed framework enables self-elicitation to elicit expressions of knowledge and understand semantics.

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Linlin Zhang, Kai Fan, Jiajun Bu, Zhongqiang Huang

LR²Bench: Evaluating Long-chain Reflective Reasoning Capabilities of Large Language Models via Constraint Satisfaction Problems (2025.findings-acl)

Challenge:	Simultaneous Speech Translation (SimulST) is a task focused on ensuring high-quality translation of speech in low-latency situations.
Approach:	They propose a token-level cross-modal alignment method to improve the translation of text to audio . they use audio transcription pairs to pre-train the encoder and a random wait-k-tokens strategy to optimize the task.
Outcome:	The proposed method achieves better trade-off between translation quality and latency.

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Jianghao Chen, Zhenlin Wei, Zhenjiang Ren, Ziyong Li, Jiajun Zhang

EfficientLLM: Unified Pruning-Aware Pretraining for Auto-Designed Compact Language Models (2026.acl-long)

Challenge:	Recent advances in o1-like models have significantly enhanced the reasoning abilities of Large Language Models (LLMs).
Approach:	They propose a benchmark to evaluate the Long-chain Reflective Reasoning capabilities of Large Language Models.
Outcome:	The proposed benchmark evaluates the Long-chain Reflective Reasoning capabilities of Large Language Models (LLMs) it consists of 850 samples across six Constraint Satisfaction Problems (CSPs)

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Xingrun Xing, Zheng Liu, Shitao Xiao, Boyan Gao, Yiming Liang, Haokun Lin, Xianlin Zeng, Guoqi Li, Jiajun Zhang

Interpreting and Exploiting Functional Specialization in Multi-Head Attention under Multi-task Learning (2023.emnlp-main)

Challenge:	Large language models (LLMs) driven by scaling laws can be developed in large model sizes.
Approach:	They propose a pruning-aware pretraining approach that decouples LLM pruning from direct pretraining.
Outcome:	The proposed model outperforms pretraining models with 100M 1B parameters in commen sense benchmarks.

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Chong Li, Shaonan Wang, Yunhao Zhang, Jiajun Zhang, Chengqing Zong

Responsible & Trustworthy NLP

Challenge:	Experimental results show that multi-head attention module evolves functional specialization after multi-task training.
Approach:	They propose a method to quantify the degree of functional specialization in multi-head attention . they propose 'multi-task training' method to increase functional specialisation and mitigate negative information transfer .
Outcome:	The proposed method increases functional specialization and mitigates negative information transfer in multi-task learning without adding any parameters.

CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization (2021.emnlp-main)

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Haitao Lin, Liqun Ma, Junnan Zhu, Lu Xiang, Yu Zhou, Jiajun Zhang, Chengqing Zong

Dialogue Systems & Conversational Agents

Challenge:	Existing summarization methods are prone to generate redundant and incoherent summaries, causing the performance to be worse.
Approach:	They propose a Chinese dataset for Customer Service Dialogue Summarization (CSDS) that provides role-oriented summaries to acquire different speakers' viewpoints.
Outcome:	The proposed dataset improves the abstractive summaries in two aspects . it also provides role-oriented summary to acquire different speakers’ viewpoints .

Improving In-context Learning of Multilingual Generative Language Models with Cross-lingual Alignment (2024.naacl-long)

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Chong Li, Shaonan Wang, Jiajun Zhang, Chengqing Zong

Synchronously Generating Two Languages with Interactive Decoding (D19-1)

Challenge:	Existing studies show that multilingual generative models exhibit a strong language bias toward high-resource languages.
Approach:	They propose a cross-lingual alignment framework exploiting pairs of translation sentences to improve cross-linguistic abilities.
Outcome:	The proposed framework improves cross-lingual abilities and mitigates performance gap.

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Yining Wang, Jiajun Zhang, Long Zhou, Yuchen Liu, Chengqing Zong

Probing Cross-modal Semantics Alignment Capability from the Textual Perspective (2022.findings-emnlp)

Challenge:	Experimental results show that multilingual NMT models handle multiple language pairs in one model.
Approach:	They propose an interactive approach to translate a source language into two different languages simultaneously and interactively.
Outcome:	The proposed approach improves on IWSLT and WMT datasets.

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Zheng Ma, Shi Zong, Mianzhi Pan, Jianbing Zhang, Shujian Huang, Xinyu Dai, Jiajun Chen

Boosting Textural NER with Synthetic Image and Instructive Alignment (2024.findings-acl)

Challenge:	In recent years, vision and language pre-training (VLP) models have advanced the state-of-the-art results in a variety of cross-modal downstream tasks.
Approach:	They propose a new probing method that is based on image captioning to first empirically study the cross-modal semantics alignment of VLP models.
Outcome:	The proposed method analyzes captions generated by five popular VLP models to reveal how well they align with visual words and how well these align with images.

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Jiahao Wang, Wenjun Ke, Peng Wang, Hang Zhang, Dong Nie, Jiajun Liu, Guozheng Li, Ziyu Shang

Dynamic Context Selection for Document-level Neural Machine Translation via Reinforcement Learning (2020.emnlp-main)

Challenge:	Named entity recognition (NER) is a key task reliant on textual data.
Approach:	They propose a method to transform NER into a multimodal task by using images from the internet as auxiliaries.
Outcome:	The proposed method surpasses all text-only baselines and improves F1 score by 1.4% to 2.3% on prominent MNER datasets.

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Xiaomian Kang, Yang Zhao, Jiajun Zhang, Chengqing Zong

Large Language Models Know What is Key Visual Entity: An LLM-assisted Multimodal Retrieval for VQA (2024.emnlp-main)

Challenge:	Existing document-level neural machine translation methods use all context sentences in a fixed scope.
Approach:	They propose an approach to select dynamic context so that document-level neural machine translation models can utilize more useful selected context sentences.
Outcome:	The proposed approach can select adaptive context sentences for different source sentences and significantly improves translation quality over sentences in a document.

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Pu Jian, Donglei Yu, Jiajun Zhang

NCLS: Neural Cross-Lingual Summarization (D19-1)

Challenge:	Existing visual language models struggle to capture longtail knowledge in the real world due to redundant visual information.
Approach:	They propose a method leveraging the reasoning capability of a large language model to identify key visual entities.
Outcome:	The proposed method outperforms other strong visual language model-based systems in two knowledge-intensive VQA benchmarks and performs comparably to models with 1-2 orders larger parameters.

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Junnan Zhu, Qian Wang, Yining Wang, Yu Zhou, Jiajun Zhang, Shaonan Wang, Chengqing Zong

Implicit Cross-Lingual Rewarding for Efficient Multilingual Preference Alignment (2025.findings-acl)

Challenge:	Existing approaches to cross-lingual summarization divide the task into two steps: summarizing and translation.
Approach:	They propose to integrate two related tasks into the training process of CLS under multi-task learning to improve cross-lingual summarization.
Outcome:	The proposed framework improves on English-to-Chinese and Chinese-to English CLS human-corrected test sets.

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Wen Yang, Junhong Wu, Chen Wang, Chengqing Zong, Jiajun Zhang

Ensure the Correctness of the Summary: Incorporate Entailment Knowledge into Abstractive Sentence Summarization (C18-1)

Challenge:	Existing approaches to align English LLMs with human preferences rely on expensive human annotations or advanced multilingual preference alignment models.
Approach:	They propose a method that captures learned preferences from English models by implicit rewards . they annotate preference relations in cross-lingual instruction-following pairs using English .
Outcome:	The proposed approach captures learned preferences from well-aligned English models by implicit rewards and transfers them to other languages through iterative training.

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Haoran Li, Junnan Zhu, Jiajun Zhang, Chengqing Zong

Summarization

Challenge:	Existing approaches focus on improving the informativeness of the summary, but ignore the correctness.
Approach:	They propose an entailment-aware encoder and an aML-based decoder to improve the correctness of the sentence summarization task.
Outcome:	The proposed model outperforms baselines on informativeness and correctness.

Bilingual Mutual Information Based Adaptive Training for Neural Machine Translation (2021.acl-short)

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Yangyifan Xu, Yijin Liu, Fandong Meng, Jiajun Zhang, Jinan Xu, Jie Zhou

RLAE: Reinforcement Learning-Assisted Ensemble for LLMs (2025.emnlp-main)

Challenge:	Existing approaches to token-level adaptive training only use static word frequency information without considering the source language.
Approach:	They propose a bilingual mutual information based adaptive objective that assigns weights to target tokens with higher BMI . they propose to use this approach to improve token-level adaptive training .
Outcome:	The proposed method improves token-level adaptive training on two languages.

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Yuqian Fu, Yuanheng Zhu, Jiajun Chai, Guojun Yin, Wei Lin, Qichao Zhang, Dongbin Zhao

Challenge:	Existing ensemble methods for ensembling large language models rely on fixed weighting strategies that fail to adapt to dynamic, context-dependent characteristics of LLMs.
Approach:	They propose a framework that reformulates LLM ensemble through a Markov Decision Process.
Outcome:	The proposed framework outperforms existing methods by 3.3% on a diverse set of tasks while achieving lower time latency.

Other Roles Matter! Enhancing Role-Oriented Dialogue Summarization via Role Interactions (2022.acl-long)

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Haitao Lin, Junnan Zhu, Lu Xiang, Yu Zhou, Jiajun Zhang, Chengqing Zong

Dialogue Systems & Conversational Agents

Challenge:	Existing methods for role-oriented dialogue summarization ignore information from other roles, resulting in omitted information.
Approach:	They propose a novel method that uses cross attention and decoder self-attention interactions to acquire other roles' critical information.
Outcome:	The proposed method significantly outperforms baselines on two public role-oriented dialogue summarization datasets.

Source Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language (C18-1)

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He Bai, Yu Zhou, Jiajun Zhang, Liang Zhao, Mei-Yuh Hwang, Chengqing Zong

Information Extraction & Text Mining

Challenge:	a study aims to develop a language transferring system to avoid the trouble of acquiring and labeling a new big SLU corpus . general-purpose translators cannot handle the lot of semantic labels, not to mention cultural differences . a RL-based language transfer method can be used to adapt the adapted translator to a target language .
Approach:	They propose to use reinforcement learning to adapt a spoken language understanding model to a target language.
Outcome:	The proposed language transferring method improves domain classification accuracy by 22% compared with naive translation . the proposed language transfer method can be used on Chinese to English translators with more proper slot tags .

Multimodal Sentence Summarization via Multimodal Selective Encoding (2020.coling-main)

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Haoran Li, Junnan Zhu, Jiajun Zhang, Xiaodong He, Chengqing Zong

Exploring Layer Activation Dynamic of CoT via Knowledge Probe (2026.acl-long)

Challenge:	Existing methods for generating summary from text and image ignore that the image can improve the ability of the encoder to identify highlights of a news event or document.
Approach:	They propose a multimodal selective gate network that takes reciprocal relationships between textual and multi-level visual features into account to select highlights of the event.
Outcome:	The proposed model can generate summary for a given sentence-image pair using visual signals . it can also capture highlights embedded in the image more accurately, the authors show .

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Chuanxin Zhang, Jiajun Liu, Yao He, Wenjun Ke, Peng Wang, Yankun Le, Sirui Liu, Zhaoyu Yang

Information Extraction & Text Mining

Challenge:	Chain-of-thought reasoning has emerged as a crucial paradigm for multi-step reasoning tasks.
Approach:	They propose a multi-stage probing framework that enforces structured reasoning with three explicit stages: keyword extraction, theorem generation, and computation execution.
Outcome:	The proposed framework enforces structured reasoning with three explicit stages: keyword extraction, theorem generation, and computation execution.

Hit the Sweet Spot! Span-Level Ensemble for Large Language Models (2025.coling-main)

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Yangyifan Xu, Jianghao Chen, Junhong Wu, Jiajun Zhang

Robustness in NLP

Challenge:	a recent study focused on sample-level and token-level ensembles, which hinder dynamic correction and enhancement of outputs during the generation process.
Approach:	They propose a span-level ensemble method that balances real-time adjustments and accurate ensemble decisions.
Outcome:	The proposed method improves performance across language generation tasks significantly.

Learning Representation Mapping for Relation Detection in Knowledge Base Question Answering (P19-1)

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Peng Wu, Shujian Huang, Rongxiang Weng, Zaixiang Zheng, Jianbing Zhang, Xiaohui Yan, Jiajun Chen

Three Strategies to Improve One-to-Many Multilingual Translation (D18-1)

Challenge:	Existing approaches to detect relation detection only get high accuracy for questions whose relations have been seen in training data.
Approach:	They propose a method to learn representation mapping for both seen and unseen relations based on previously learned relation embedding.
Outcome:	The proposed method improves the performance of unseen relations while keeping the performance comparable to the state-of-the-art.

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Yining Wang, Jiajun Zhang, Feifei Zhai, Jingfang Xu, Chengqing Zong

From Completion to Editing: Unlocking Context-Aware Code Infilling via Search-and-Replace Instruction Tuning (2026.acl-long)

Challenge:	Existing studies show that one-to-many multilingual translation cannot perform on par with the individually trained models.
Approach:	They propose to exploit unique initial states for target languages and language-dependent positional embeddings to create hidden cells of the encoder to achieve comparable or even better performance than individually trained models.
Outcome:	The proposed methods achieve comparable or even better performance than the individually trained models.

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Jiajun Zhang, Zeyu Cui, Jiaxi Yang, Lei Zhang, Yuheng Jing, Zeyao Ma, Tianyi Bai, Zilei Wang, Qiang Liu, Liang Wang, Binyuan Hui, Junyang Lin

Programming Languages in NLP

Challenge:	Fill-in-the-Middle (FIM) models suffer from performance degradation and prohibitive latency.
Approach:	They propose a search-and-replace infilling framework that integrates agentic verification and editing into a single-pass inference process.
Outcome:	The proposed framework harmonizes completion tasks with the instruction-following priors of Chat LLMs, extending the paradigm from static infilling to dynamic context-aware editing.

LoRETTA: Low-Rank Economic Tensor-Train Adaptation for Ultra-Low-Parameter Fine-Tuning of Large Language Models (2024.naacl-long)

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Yifan Yang, Jiajun Zhou, Ngai Wong, Zheng Zhang

Bridging the Gap between Different Vocabularies for LLM Ensemble (2024.naacl-long)

Challenge:	Existing methods for parameter-efficient fine-tuning are limited by the growing number of trainable parameters with the rapid deployment of Large Language Models (LLMs).
Approach:	They propose a parameter-efficient framework that reduces trainable parameters through tensor-train decomposition.
Outcome:	The proposed methods achieve comparable or better performance than most widely used methods with up to 100 fewer parameters on the LLaMA-2-7B models.

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Yangyifan Xu, Jinliang Lu, Jiajun Zhang

COIG-CQIA: Quality is All You Need for Chinese Instruction Fine-tuning (2025.findings-naacl)

Challenge:	Existing methods to combine large language models with complete outputs have limited effectiveness . lexical gaps between different LLMs hinder dynamic correction and enhancement .
Approach:	They propose a method to ensemble large language models via Vocabulary Alignment (EVA) they learn mappings between vocabularies of different LLMs with overlapping tokens .
Outcome:	The proposed method bridges the lexical gap among various LLMs, enabling meticulous ensemble at each generation step.

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Yuelin Bai, Xeron Du, Yiming Liang, Leo Jin, Junting Zhou, Ziqiang Liu, Feiteng Fang, Mingshan Chang, Tianyu Zheng, Xincheng Zhang, Nuo Ma, Zekun Moore Wang, Ruibin Yuan, Haihong Wu, Hongquan Lin, Wenhao Huang, Jiajun Zhang, Chenghua Lin, Jie Fu, Min Yang, Shiwen Ni, Ge Zhang

From Scores to Preferences: Redefining Evaluation Paradigm for Speech Quality Reward Modeling (2026.findings-acl)

Challenge:	Existing datasets for Chinese instruction tuning are not well-aligned with Chinese users’ interaction patterns.
Approach:	They propose to use Chinese instruction tuning datasets to improve instruction fine-tuning for Chinese users.
Outcome:	The proposed dataset shows that Chinese models achieve competitive performance in diverse benchmarks.

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Yifei Cao, Changhao Jiang, Jiabao Zhuang, Jiajun Sun, Ming Zhang, Zhiheng Xi, Hui Li, Shihan Dou, Yuran Wang, Yunke Zhang, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

UCoder: Unsupervised Code Generation by Internal Probing of Large Language Models (2026.findings-acl)

Challenge:	Experimental results show that the MOS-aware GRM significantly improves fine-grained speech quality discrimination.
Approach:	They propose a MOS-aware reward model that incorporates MOS gap into reward function during reinforcement learning.
Outcome:	The proposed model significantly improves fine-grained speech quality discrimination.

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Jiajun Wu, Jian Yang, Wei Zhang, Linzheng Chai, Yuchi Ma, Ensheng Shi, Yuqing Ma, Zhoujun Li, Xianglong Liu

Mitigating Object Hallucinations in MLLMs via Multi-Frequency Perturbations (2025.findings-emnlp)

Challenge:	Large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks, but their effectiveness relies on supervised training with extensive labeled data and computational resources.
Approach:	They propose an unsupervised method that leverages Internal Probing of Large language models for Code generation without any external corpus, even unlabeled code snippets.
Outcome:	The proposed method can achieve competitive performance compared to supervised approaches while reducing the dependency on labeled data and computational resources.

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Shuo Li, Jiajun Sun, Guodong Zheng, Xiaoran Fan, Yujiong Shen, Yi Lu, Zhiheng Xi, Yuming Yang, Wenming Tan, Tao Ji, Tao Gui, Qi Zhang, Xuanjing Huang

LAGCL4Rec: When LLMs Activate Interactions Potential in Graph Contrastive Learning for Recommendation (2025.findings-emnlp)

Challenge:	Multimodal large language models have demonstrated remarkable performance in visual-language tasks, but their authenticity is often compromised by object hallucinations.
Approach:	They propose a multi-frequency perturbation method that leverages both low-frequency and high-frequency features of images to perturb visual feature representations and explicitly suppress redundant frequency-domain features during inference.
Outcome:	The proposed method significantly mitigates object hallucinations across various model architectures.

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Leqi Zheng, Chaokun Wang, Canzhi Chen, Jiajun Zhang, Cheng Wu, Zixin Song, Shannan Yan, Ziyang Liu, Hongwei Li

Embodied-Reasoner: Synergizing Visual Search, Reasoning, and Action for Embodied Interactive Tasks (2026.acl-long)

Challenge:	Traditional contrastive learning methods treat negative feedback as equally hard or easy, ignoring informative semantic difficulty during training.
Approach:	They propose a framework leveraging Large Language Models to Activate interactions in Graph Contrastive Learning for Recommendation.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks on multiple benchmarks.

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Wenqi Zhang, Mengna Wang, Gangao Liu, Huixin Xu, Yiwei Jiang, Yongliang Shen, Guiyang Hou, Zhe Zheng, Hang Zhang, Xin Li, Jiajun Liu, Weiming Lu, Peng Li, Yueting Zhuang

CoTJudger: A Graph-Driven Framework for Automatic Evaluation of Chain-of-Thought Efficiency and Redundancy in LRMs (2026.findings-acl)

Challenge:	Recent advances in reasoning models have demonstrated remarkable capabilities on mathematical and coding tasks, but their effectiveness in embodied domains remains largely unexplored.
Approach:	They propose a reasoning model for interactive embodied tasks that synthesizes 9.3k coherent Observation-Thought-Action trajectories containing 64k ego-centric images and 90k diverse reasoning processes.
Outcome:	The proposed model outperforms existing visual reasoning models by +9%, 24%, and +13% on long-horizon tasks.

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Siyi Li, Jiajun Shi, Shiwen Ni, Ge Zhang, Shuaimin Li, Shijian Wang, Zhoufutu Wen, Yizhi LI, Hamid Alinejad-Rokny, Jiaheng Liu, Min Yang, Wenhao Huang

Green & Sustainable NLP

Challenge:	Existing evaluations emphasize final accuracy or coarse token counts, and lack automated tools to separate essential logic from structural redundancy.
Approach:	They propose a graph-driven framework that quantifies reasoning efficiency by converting free-form CoTs into directed dependency graphs and extracting the Shortest Effective Path needed to reach a correct solution.
Outcome:	Evaluating 21 LRMs, the proposed framework quantifies reasoning efficiency by converting free-form CoTs into directed dependency graphs and extracting the Shortest Effective Path (SEP) needed to reach a correct solution.

Improving Unsupervised Neural Machine Translation via Training Data Self-Correction (2024.lrec-main)

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Jinliang Lu, Jiajun Zhang

Attention Calibration for Transformer in Neural Machine Translation (2021.acl-long)

Challenge:	Unsupervised neural machine translation models can generate mistakes during training . however, the quality of pseudo-parallel sentences cannot be guaranteed .
Approach:	They propose a method to improve the quality of pseudo-parallel sentences . they use token-level translations to correct mis-translated tokens .
Outcome:	Empirical results show that the proposed method outperforms baselines on widely used datasets.

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Yu Lu, Jiali Zeng, Jiajun Zhang, Shuangzhi Wu, Mu Li

MSMO: Multimodal Summarization with Multimodal Output (D18-1)

Challenge:	Attention mechanisms have been ubiquitous in neural machine translation (NMT) however, many studies doubt whether highlyattended inputs have a large impact on the model outputs.
Approach:	They propose to introduce a mask perturbation model that automatically evaluates each input’s contribution to the model outputs.
Outcome:	The proposed model is more uniform at lower layers while more concentrated on the specific inputs at higher layers.

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Junnan Zhu, Haoran Li, Tianshang Liu, Yu Zhou, Jiajun Zhang, Chengqing Zong

Beyond Scaling: Measuring and Predicting the Upper Bound of Knowledge Retention in Language Model Pre-Training (2026.acl-long)

Challenge:	Existing studies show that multimodal summarization can improve user satisfaction for informativeness of summaries by using information in visual modality.
Approach:	They propose a task to generate text and select the most relevant image from the multimodal input and a novel multimodal automatic evaluation method to evaluate multimodal outputs.
Outcome:	The proposed method improves user satisfaction by 12.4% compared to the current system .

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Changhao Jiang, Ming Zhang, Yifei Cao, Junjie Ye, Xiaoran Fan, Shihan Dou, Zhiheng Xi, Jiajun Sun, Yi Dong, Yujiong Shen, Jingqi Tong, Baoyu Fan, Tao Gui, Qi Zhang, Xuanjing Huang

Improving Long-Context Translation via Self-Supervised Dual Learning (2026.acl-long)

Challenge:	Existing methods to predict performance of large language models are lacking . authors propose a size-dependent mutual information predictor for closed-book question answering accuracy .
Approach:	They propose a size-dependent mutual information predictor that integrates knowledge frequency, knowledge specificity, and model size to forecast closed-book question answering accuracy.
Outcome:	The proposed method outperforms baseline models and achieves R2 > 0.7 in predicting QA accuracy without additional training.

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Shanbo Cheng, Shuaijie She, Yu Bao, Jianbing Zhang, Jiajun Chen, Shujian Huang

Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation (2021.findings-emnlp)

Challenge:	Large language models with long context windows suffer from catastrophic information distortion, undermining the strict faithfulness required for translation.
Approach:	They propose a self-supervised post-training framework that improves long-document translation reliability via round-trip consistency.
Outcome:	The proposed framework improves long-document translation reliability via round-trip consistency.

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Xin Zheng, Zhirui Zhang, Shujian Huang, Boxing Chen, Jun Xie, Weihua Luo, Jiajun Chen

Look Again, Think Slowly: Enhancing Visual Reflection in Vision-Language Models (2025.emnlp-main)

Challenge:	kNN-MT is a non-parametric method that uses nearest neighbor retrieval to translate out-of-domain sentences, rare words, etc.
Approach:	They propose a framework that directly uses in-domain monolingual sentences to build an effective datastore for k-nearest-neighbor retrieval.
Outcome:	The proposed framework improves translation accuracy with target-side monolingual data while achieving comparable performance with back-translation.

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Pu Jian, Junhong Wu, Wei Sun, Chen Wang, Shuo Ren, Jiajun Zhang