Papers by Yong Jiang

Risk Minimization for Zero-shot Sequence Labeling (2021.acl-long)

Challenge:	Existing neural paraphrase generation methods focus on single paraphrases while ignoring the fact that diversity is essential for enhancing generalization capability and robustness of downstream applications.
Approach:	They propose a novel approach with two discriminators and multiple generators to generate a variety of different paraphrases.
Outcome:	The proposed model gains significant diversity and improves quality over state-of-the-art datasets.

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Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Towards Real-World Writing Assistance: A Chinese Character Checking Benchmark with Faked and Misspelled Characters (2024.acl-long)

Challenge:	Existing approaches to zero-shot sequence labeling are expensive and hard to obtain for lowresource languages/domains.
Approach:	They propose a framework for zero-shot sequence labeling with minimum risk training and a decomposable risk function that models the relations between predicted labels from the source models and the true labels.
Outcome:	The proposed framework outperforms state-of-the-art systems on 21 datasets.

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Yinghui Li, Zishan Xu, Shaoshen Chen, Haojing Huang, Yangning Li, Shirong Ma, Yong Jiang, Zhongli Li, Qingyu Zhou, Hai-Tao Zheng, Ying Shen

Visual Data in NLP

Challenge:	Existing studies focus on misspelled characters, ignoring faked characters which are more common and difficult to correct.
Approach:	They propose to use Chinese character checking to identify and correct wrong characters in texts by human annotation.
Outcome:	The proposed dataset is the first real-world visual and the largest human-crafted dataset for the Chinese character checking scenario.

Second-Order Unsupervised Neural Dependency Parsing (2020.coling-main)

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Songlin Yang, Yong Jiang, Wenjuan Han, Kewei Tu

Exploring Key Point Analysis with Pairwise Generation and Graph Partitioning (2024.naacl-long)

Challenge:	supervised dependency parsers can reach a very high accuracy, but they require treebanks for training.
Approach:	They propose a second-order extension of unsupervised neural dependency models that incorporate grandparent-child or sibling information.
Outcome:	The proposed model achieves 10% improvement over the previous state-of-the-art model on the full WSJ dataset.

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Xiao Li, Yong Jiang, Shen Huang, Pengjun Xie, Gong Cheng, Fei Huang

Structured Data in NLP

Challenge:	Existing methods for key point analysis rely on semantic similarity instead of measuring the existence of shared key points .
Approach:	They propose a key point analysis approach with pairwise generation and graph partitioning to summarize arguments into a concise set of key points.
Outcome:	The proposed model surpasses existing models on ArgKP and QAM datasets.

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts (2022.acl-demo)

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Stephen Bach, Victor Sanh, Zheng Xin Yong, Albert Webson, Colin Raffel, Nihal V. Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, Zaid Alyafeai, Manan Dey, Andrea Santilli, Zhiqing Sun, Srulik Ben-david, Canwen Xu, Gunjan Chhablani, Han Wang, Jason Fries, Maged Al-shaibani, Shanya Sharma, Urmish Thakker, Khalid Almubarak, Xiangru Tang, Dragomir Radev, Mike Tian-jian Jiang, Alexander Rush

DecoupleSearch: Decouple Planning and Search via Hierarchical Reward Modeling (2025.emnlp-main)

Challenge:	PromptSource is a system for creating, sharing, and using natural language prompts . prompts are used to train and query language models in zero-shot learning settings .
Approach:	PromptSource is a system for creating, sharing, and using natural language prompts . et al.: using prompts to train and query language models is emerging area in NLP . they propose a templating language for defining data-linked prompts, a user interface that iterates on prompt development .
Outcome:	PromptSource is a system for creating, sharing, and using natural language prompts . it has a templating language for defining data-linked prompts and a community-driven set of guidelines .

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Hao Sun, Zile Qiao, Bo Wang, Guoxin Chen, Yingyan Hou, Yong Jiang, Pengjun Xie, Fei Huang, Yan Zhang

Reasoning

Challenge:	Retrieval-Augmented Generation (RAG) systems have emerged as a pivotal methodology for enhancing Large Language Models (LLMs).
Approach:	They propose a framework that decouples planning and search processes using dual value models, enabling independent optimization of plan reasoning and search grounding.
Outcome:	The proposed framework decouples planning and search processes using dual value models, enabling independent optimization of plan reasoning and search grounding.

Towards General Agentic Intelligence via Environment Scaling (2026.findings-acl)

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Runnan Fang, Shihao Cai, Baixuan Li, Jialong Wu, Guangyu Li, Wenbiao Yin, Xinyu Wang, Xiaobin Wang, Liangcai Su, Zhen Zhang, Shibin Wu, Zhengwei Tao, Yong Jiang, Pengjun Xie, Ningyu Zhang, Fei Huang, Wentao Zhang, Jingren Zhou

An Empirical Comparison of Unsupervised Constituency Parsing Methods (2020.acl-main)

Challenge:	Diverse real-world APIs require precise, robust function-calling intelligence, which needs agents to develop these capabilities through interaction in varied environments.
Approach:	They propose a framework that scales up environments to enable agentic intelligence . they use a two-phase agent fine-tuning strategy to first endow agents with basic agentic capabilities, then specializing them for domain-specific contexts.
Outcome:	Experiments on -bench, -Bench, and ACEBench show that the model significantly enhances the models’ function-calling capability.

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Jun Li, Yifan Cao, Jiong Cai, Yong Jiang, Kewei Tu

Mitigating the Alignment Tax of RLHF (2024.emnlp-main)

Challenge:	Existing methods for unsupervised constituency parsing are inconsistent due to data preprocessing, lexicalization, and evaluation metrics.
Approach:	They propose to standardize experimental settings for better comparability between methods . they compare existing methods with those proposed by decade-old models .
Outcome:	The proposed methods perform better than decade-old models on English and Japanese, respectively, compared with decade- old models.

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Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang

Word Reordering for Zero-shot Cross-lingual Structured Prediction (2021.emnlp-main)

Challenge:	Large Language Models (LLMs) acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, which is also known as the alignment tax.
Approach:	They propose to use a model averaging technique to find the most powerful alignment-forging Pareto front among RLHF algorithms.
Outcome:	The proposed method achieves the strongest alignment-forging Pareto front among competing methods.

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Tao Ji, Yong Jiang, Tao Wang, Zhongqiang Huang, Fei Huang, Yuanbin Wu, Xiaoling Wang

Reasoning under Uncertainty: Efficient LLM Inference via Unsupervised Confidence Dilution and Convergent Adaptive Sampling (2025.emnlp-main)

Challenge:	Current sentence encoders are word order sensitive, resulting in poor performance . Adapting word order from one language to another is key in cross-lingual structured prediction.
Approach:	They propose a new module to organize words following the source language order . they build structured prediction models with bag-of-words inputs and introduce a module to do this .
Outcome:	The proposed model significantly improves target language performance for languages that are distant from the source language.

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Zhenning Shi, Yijia Zhu, Yi Xie, Junhan Shi, Guorui Xie, Haotian Zhang, Yong Jiang, Congcong Miao, Qing Li

EGSS: Entropy-guided Stepwise Scaling for Reliable Software Engineering (2026.acl-long)

Challenge:	Large language models suffer from overconfidence and computational inefficiency due to fixed computation budgets and miscalibrated confidence estimates.
Approach:	They propose a framework for computationally efficient, trustworthy reasoning under uncertainty using Diversity-Aware Self-Signal Dilution and Convergent Adaptive Weighted Sampling techniques.
Outcome:	The proposed framework reduces inference cost by 70% while maintaining accuracy levels while reducing inference costs.

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Chenhui Mao, Yuanting Lei, Zhixiang Wei, Ming Liang, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li

Domain-Specific NER via Retrieving Correlated Samples (2022.coling-1)

Challenge:	Entropy-Guided Stepwise Scaling (EGSS) is a novel TTS framework for software engineering tasks.
Approach:	They propose an entropy-guided stepwise scaling framework that balances efficiency and effectiveness through entropic-guide encoding and robust test-suite augmentation.
Outcome:	EGSS boosts performance by 5–10% across all evaluated models, and reduces inference-time token usage by over 28% . compared to existing methods, EGS reduces token usage and reduce inference time by over 20% .

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Xin Zhang, Yong Jiang, Xiaobin Wang, Xuming Hu, Yueheng Sun, Pengjun Xie, Meishan Zhang

SARA: Salience-Aware Reinforced Adaptive Decoding for Large Language Models in Abstractive Summarization (2025.acl-long)

Challenge:	Successful Named Entity Recognition models fail on texts from some special domains, for example, Chinese addresses and e-commerce titles.
Approach:	They propose to enhance NER models with correlated samples to help the text understanding . they draw correlated texts by the sparse BM25 retriever from large-scale in-domain unlabeled data .
Outcome:	Empirical results show that NER models can be enhanced with correlated samples . the proposed model can be used to reason out the correct answer on hard cases .

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Nayu Liu, Junnan Zhu, Yiming Ma, Zhicong Lu, Wenlei Xu, Yong Yang, Jiang Zhong, Kaiwen Wei

COMBO: A Complete Benchmark for Open KG Canonicalization (2023.eacl-main)

Challenge:	Existing decoding strategies neglect the explicit use of salient contextual information and rely on static hyperparameters to fix the balance between contextual and prior knowledge.
Approach:	They propose a salience-aware reinforced adaptive decoding (SARA) which incorporates salient contextual information and allows the model to determine reliance on source document's context, salient context, and model's prior knowledge based on pointwise mutual information.
Outcome:	The proposed model improves the quality and faithfulness of summaries across LLMs without modifying their weights.

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Chengyue Jiang, Yong Jiang, Weiqi Wu, Yuting Zheng, Pengjun Xie, Kewei Tu

KBM: Delineating Knowledge Boundary for Adaptive Retrieval in Large Language Models (2025.findings-emnlp)

Challenge:	Existing datasets for open KG canonicalization only provide gold entity-level canonization for noun phrases.
Approach:	They propose a complete benchmark for open KG canonicalization that provides gold ontology-level canonization for relation phrases and source sentences for extraction.
Outcome:	The proposed method improves relation canonicalization and ontology-level canonization of the noun phrase.

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Zhen Zhang, Xinyu Wang, Yong Jiang, Zile Qiao, Zhuo Chen, Guangyu Li, Feiteng Mu, Mengting Hu, Pengjun Xie, Fei Huang

Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks (2023.findings-acl)

Challenge:	Retrieval-augmented generation (RAG) is employed to tackle these challenges . a Knowledge Boundary Model (KBM) is used to express the known/unknown of a given question .
Approach:	They propose a Knowledge Boundary Model to express the known/unknown of a given question . they find that not all questions need to trigger RAG to improve performance .
Outcome:	The proposed model reduces time and computational costs by retrieving parts of unknown knowledge . the proposed model can express the known/unknown of a given question and determine whether a RAG needs to be triggered .

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Xuming Hu, Yong Jiang, Aiwei Liu, Zhongqiang Huang, Pengjun Xie, Fei Huang, Lijie Wen, Philip S. Yu

Improving Named Entity Recognition by External Context Retrieving and Cooperative Learning (2021.acl-long)

Challenge:	Existing augmentation techniques manipulate words in the original text that break the semantic coherence of the text, or exploit generative models that ignore preserving entities in the text.
Approach:	They propose a novel Entity-to-Text based data augmentation technique called EnTDA to add, delete, replace or swap entities in the original text.
Outcome:	The proposed technique generates semantically coherent and entity preserving texts on thirteen NER tasks and two settings.

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Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Convert Language Model into a Value-based Strategic Planner (2025.acl-industry)

Challenge:	Recent work shows document-level contexts can significantly improve Named Entity Recognition models.
Approach:	They propose to find external contexts of a sentence by retrieving and selecting a set of semantically relevant texts through a search engine with the original sentence as the query.
Outcome:	The proposed approach can achieve new state-of-the-art performance on 8 NER data sets across 5 domains.

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Xiaoyu Wang, Yue Zhao, Qingqing Gu, Zhonglin Jiang, Yong Chen, Luo Ji

OmniThink: Expanding Knowledge Boundaries in Machine Writing through Thinking (2025.emnlp-main)

Challenge:	Emotional support conversation (ESC) aims to alleviate the emotional distress of individuals through effective conversations.
Approach:	They propose a framework that bootstraps the planning during ESC and determines the optimal strategy based on long-term returns.
Outcome:	The proposed framework outperforms baseline models on ESC datasets and can be used to guide the LLM to response.

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Zekun Xi, Wenbiao Yin, Jizhan Fang, Jialong Wu, Runnan Fang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

Exploring and Adapting Chinese GPT to Pinyin Input Method (2022.acl-long)

Challenge:	Recent advances in Large Language Models (LLMs) have demonstrated remarkable progress in machine writing such as open domain long-form generation.
Approach:	They propose a slow-thinking machine writing framework that emulates the human-like process of iterative expansion and reflection to improve the knowledge density of generated articles.
Outcome:	The proposed framework improves the knowledge density of generated articles without compromising metrics such as coherence and depth.

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Minghuan Tan, Yong Dai, Duyu Tang, Zhangyin Feng, Guoping Huang, Jing Jiang, Jiwei Li, Shuming Shi

No More Stale Feedback: Co-Evolving Critics for Open-World Agent Learning (2026.acl-long)

Challenge:	a frozen GPT can generate state-of-the-art performance on perfect pinyin, but performance drops when input includes abbreviated pinyan, which links to even larger number of Chinese characters.
Approach:	They propose to use Chinese GPT to generate fluent sentences using abbreviated pinyin.
Outcome:	The proposed approach improves on abbreviated pinyin across all domains.

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Zhicong Li, Lingjie Jiang, Yulan Hu, Xingchen Zeng, Yixia Li, Xiangwen Zhang, Guanhua Chen, Zheng Pan, Xin Li, Yong Liu

Challenge:	Current methods for training Large Language Model agents rely on static or offline critic models, which fail to adapt as the policy evolves.
Approach:	They propose a framework that integrates a critique and a policy to optimize the policy and critic through a synchronized co-evolutionary loop.
Outcome:	The proposed framework yields more stable training and higher long-horizon task success across open-world environments.

Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor (2021.acl-long)

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Xinyu Wang, Yong Jiang, Zhaohui Yan, Zixia Jia, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Named Entity and Relation Extraction with Multi-Modal Retrieval (2022.findings-emnlp)

Challenge:	Knowledge distillation is a technique to transfer knowledge between models, typically from a large model (the teacher) to a more fine-grained one (the student).
Approach:	They propose a factorized form of the knowledge distillation objective for structured prediction which is tractable for many typical choices of the teacher and student models.
Outcome:	The proposed model is able to transfer knowledge between teacher and student models without loss of accuracy under four different scenarios.

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Xinyu Wang, Jiong Cai, Yong Jiang, Pengjun Xie, Kewei Tu, Wei Lu

Dream to Chat: Model-based Reinforcement Learning on Dialogues with User Belief Modeling (2025.findings-emnlp)

Challenge:	Existing approaches to name entity recognition and relation extraction are knowledge-based and may not be highly relevant.
Approach:	They propose a multi-modal named entity recognition framework that leverages image information to improve the performance of NER and relation extraction.
Outcome:	The proposed framework can achieve state-of-the-art on four multi-modal named entity recognition datasets and one multi-module relation extraction dataset.

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Yue Zhao, Xiaoyu Wang, Dan Wang, Zhonglin Jiang, Qingqing Gu, Teng Chen, Ningyuan Xi, Jinxian Qu, Yong Chen, Luo Ji

Dialogue Systems & Conversational Agents

Challenge:	a framework for constructing dialogue world models for natural language tasks is currently lacking.
Approach:	They propose a framework that can be used to train a dialogue world model.
Outcome:	The proposed framework can predict future utterances and user beliefs . it can achieve state-of-the-art performance on emotion classification and sentiment identification .

EvolveSearch: An Iterative Self-Evolving Search Agent (2025.emnlp-main)

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Ding-Chu Zhang, Yida Zhao, Jialong Wu, Liwen Zhang, Baixuan Li, Wenbiao Yin, Yong Jiang, Yu-Feng Li, Kewei Tu, Pengjun Xie, Fei Huang

Balancing Speciality and Versatility: a Coarse to Fine Framework for Supervised Fine-tuning Large Language Model (2024.findings-acl)

Challenge:	Existing approaches to enabling LLM web search proficiency struggle with data production in open-search domains, while supervised fine-tuning struggles with data utilization efficiency.
Approach:	They propose an iterative self-evolution framework that combines SFT and RL to enhance agentic web search capabilities without external human-annotated reasoning data.
Outcome:	EvolveSearch achieves 4.7% improvement over current state-of-the-art in seven benchmarks . supervised fine-tuning struggles with data production in open-search domains compared with RL .

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Hengyuan Zhang, Yanru Wu, Dawei Li, Sak Yang, Rui Zhao, Yong Jiang, Fei Tan

Nested Browser-Use Learning for Agentic Information Seeking (2026.acl-long)

Challenge:	Aligned Large Language Models exhibit remarkable versatility, capable of handling diverse real-world tasks.
Approach:	They propose a coarse to fine framework to fine-tune aligned Large Language Models to achieve a balance between speciality and versatility.
Outcome:	The proposed framework outperforms baseline methods across diverse tasks and model scales.

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Baixuan Li, Jialong Wu, Wenbiao Yin, Kuan Li, Zhongwang Zhang, Huifeng Yin, Zhengwei Tao, Liwen Zhang, Pengjun Xie, Jingren Zhou, Yong Jiang, Wentao Zhang, Zhiqiang Gao

Challenge:	Existing information-seeking (IS) agents rely on the web for their information acquisition.
Approach:	They propose a browser-action framework that decouples interaction control from page exploration through a nested structure.
Outcome:	Empirical results show that NestBrowse offers clear benefits in practice.

Automated Concatenation of Embeddings for Structured Prediction (2021.acl-long)

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Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Representation Learning

Challenge:	Recent work shows that better word representations can be obtained by concatenating different types of embeddings.
Approach:	They propose to automate the process of finding better concatenated embeddings for structured prediction tasks by concatending different types of embeddables.
Outcome:	The proposed approach outperforms baselines and achieves state-of-the-art with fine-tuned embeddings on 6 tasks and 21 datasets.

AdvAug: Robust Adversarial Augmentation for Neural Machine Translation (2020.acl-main)

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Yong Cheng, Lu Jiang, Wolfgang Macherey, Jacob Eisenstein

Generalized Supervised Attention for Text Generation (2021.findings-acl)

Challenge:	Recent work in neural machine translation has led to dramatic improvements in both research and commercial systems.
Approach:	They propose a adversarial augmentation method for Neural Machine Translation that minimizes vicinal risk over virtual sentences . they use a novel vicinity distribution for adversarials to describe a smooth interpolated embedding space .
Outcome:	The proposed method outperforms the current method on Chinese-English, English-French, and English-German translation benchmarks.

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Yixian Liu, Liwen Zhang, Xinyu Zhang, Yong Jiang, Yue Zhang, Kewei Tu

Multi-View Cross-Lingual Structured Prediction with Minimum Supervision (2021.acl-long)

Challenge:	Existing supervised attention methods that use human knowledge to learn better alignments are costly or infeasible.
Approach:	They propose a generalized supervised attention method based on quasi alignments that are easier to obtain than ideal alignments.
Outcome:	The proposed framework improves generation performance and is robust against errors in attention supervision.

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Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Multilinguality

Challenge:	Existing work on cross-lingual transfer learning focuses on transferring knowledge from high-resource languages to low-resourced ones.
Approach:	They propose a multi-view framework that integrates multiple source models into an aggregated source view and transfers it to a target view based on a task-specific model.
Outcome:	The proposed framework improves on three structured prediction tasks on 16 datasets.

Diversifying Dialog Generation via Adaptive Label Smoothing (2021.acl-long)

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Yida Wang, Yinhe Zheng, Yong Jiang, Minlie Huang

Dialogue Systems & Conversational Agents

Challenge:	Existing approaches to dialog generation suffer from the over-confidence issue, which leads to poor generation diversity.
Approach:	They propose an Adaptive Label Smoothing approach that can adaptively estimate a target label distribution at each time step for different dialog contexts.
Outcome:	The proposed approach outperforms competing models on two benchmark datasets in producing diverse responses.

WebWalker: Benchmarking LLMs in Web Traversal (2025.acl-long)

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Jialong Wu, Wenbiao Yin, Yong Jiang, Zhenglin Wang, Zekun Xi, Runnan Fang, Linhai Zhang, Yulan He, Deyu Zhou, Pengjun Xie, Fei Huang

A Unified Encoding of Structures in Transition Systems (2021.emnlp-main)

Challenge:	Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of natural language processing tasks.
Approach:	They propose a benchmark to assess the ability of LLMs to perform web traversal by using an explore-critic paradigm.
Outcome:	The proposed framework mimics human-like web navigation through an explore-critic paradigm and demonstrates the effectiveness of RAG combined with WebWalker in real-world scenarios.

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Tao Ji, Yong Jiang, Tao Wang, Zhongqiang Huang, Fei Huang, Yuanbin Wu, Xiaoling Wang

Towards Emotional Support Dialog Systems (2021.acl-long)

Challenge:	Existing approaches to encode dynamic structures only encode partial information of structures.
Approach:	They propose a new attention-based encoder unifying all structures in a transition system.
Outcome:	The proposed method significantly improves the test speed and achieves the best transition-based model.

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Siyang Liu, Chujie Zheng, Orianna Demasi, Sahand Sabour, Yu Li, Zhou Yu, Yong Jiang, Minlie Huang

Dialogue Systems & Conversational Agents

Challenge:	Emotional support is a crucial ability for many conversation scenarios, including social interactions, mental health support, and customer service chats.
Approach:	They propose an Emotional Support Conversation task and an ESC Framework to train emotional support into dialog systems.
Outcome:	The proposed framework provides an example of an Emotional Support Conversation task and shows that it is more effective than existing models.

Detecting Knowledge Boundary of Vision Large Language Models by Sampling-Based Inference (2025.emnlp-main)

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Zhuo Chen, Xinyu Wang, Yong Jiang, Zhen Zhang, Xinyu Geng, Pengjun Xie, Fei Huang, Kewei Tu

Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modeling (2025.findings-naacl)

Challenge:	Existing methods to detect the knowledge boundary of Vision Large Language Models (VLLMs) are expensive and require indiscriminate retrieval to address questions that require real-time information or are knowledge-intensive.
Approach:	They propose a method that fine-tunes a VLLM on an automatically constructed dataset for boundary identification.
Outcome:	The proposed method reduces indiscriminate retrieval while maintaining or improving the performance of a VLLM on an automatically constructed dataset.

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Zile Qiao, Wei Ye, Yong Jiang, Tong Mo, Pengjun Xie, Weiping Li, Fei Huang, Shikun Zhang

Dynamic Multistep Reasoning based on Video Scene Graph for Video Question Answering (2022.naacl-main)

Challenge:	Recent advances in large language models (LLMs) have significantly enhanced their performance in various natural language processing tasks.
Approach:	They propose a robust and pluggable knowledge rewriter that is optimized for LLM generation by supporting the model's supportiveness.
Outcome:	The proposed model can be used to rewrite knowledge in a supervised manner.

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Jianguo Mao, Wenbin Jiang, Xiangdong Wang, Zhifan Feng, Yajuan Lyu, Hong Liu, Yong Zhu

Multimodality

Challenge:	Existing video QA models lack the capacity for deep video understanding and flexible multistep reasoning.
Approach:	They propose a video question answering model which performs dynamic multistep reasoning between questions and videos.
Outcome:	The proposed model improves on three widely used video QA datasets and displays better interpretability by backtracing along with the attention mechanisms to the video scene graphs.

Multilingual Grammar Induction with Continuous Language Identification (D19-1)

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Wenjuan Han, Ge Wang, Yong Jiang, Kewei Tu

A Regularization-based Framework for Bilingual Grammar Induction (D19-1)

Challenge:	Existing methods to induce grammars of multiple languages do not consider language similarity measures.
Approach:	They propose a universal grammar induction approach that captures similarity between languages . they use vector representations to capture similarity and softly tie grammar parameters .
Outcome:	The proposed approach performs well over monolingual and multilingual datasets.

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Yong Jiang, Wenjuan Han, Kewei Tu

Syntactic Text Processing

Challenge:	Existing multilingual grammar induction methods require external resources such as parallel corpora, word alignments or linguistic phylogenetic trees.
Approach:	They propose a framework in which the learning process of the grammar model of one language is influenced by knowledge from the model of another language.
Outcome:	The proposed method outperforms baselines on transfer grammar induction and bilingual grammar inducing on multiple languages.

Recall, Expand, and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing (2023.acl-long)

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Chengyue Jiang, Wenyang Hui, Yong Jiang, Xiaobin Wang, Pengjun Xie, Kewei Tu

Towards Generating Controllable and Solvable Geometry Problem by Leveraging Symbolic Deduction Engine (2025.acl-industry)

Challenge:	State-of-the-art (SOTA) methods use the cross-encoder architecture to concatenate a mention (and its context) with each type and feed it into a pretrained language model (PLM) to score their relevance.
Approach:	They propose to perform entity typing in a recall-expand-filter manner and use a novel model to encode and score all these K candidates in one forward pass.
Outcome:	The proposed method is thousands of times faster than the CE-based architecture and is very efficient in fine-grained (130 types) and coarse-grain (9 types) entity typing.

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Zhuoxuan Jiang, Tianyang Zhang, Peiyan Peng, Jing Chen, Yinong Xun, Haotian Zhang, Lichi Li, Yong Li, Shaohua Zhang

ITA: Image-Text Alignments for Multi-Modal Named Entity Recognition (2022.naacl-main)

Challenge:	Compared to math word problems, geometry problems emphasize multi-modal formats and the translation between informal and formal languages.
Approach:	They propose a symbolic deduction engine-based geometry problem generation framework that leverages a symbolic deduction engine to generate geometry problems.
Outcome:	The proposed method avoids inherent biases in translating natural language into formal language and guarantees to control the generated problems in terms of knowledge points and difficulties by an elaborate checking function.

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Xinyu Wang, Min Gui, Yong Jiang, Zixia Jia, Nguyen Bach, Tao Wang, Zhongqiang Huang, Kewei Tu

ProductAgent: Benchmarking Conversational Product Search Agent with Asking Clarification Questions (2025.emnlp-industry)

Challenge:	Recent work on Multi-modal Named Entity Recognition (MNER) relies on image information to model interactions between image and text representations.
Approach:	They propose to align image features into the textual space to better utilize attention mechanisms . they use regional object tags, captions and optical characters as visual contexts .
Outcome:	The proposed model can achieve state-of-the-art accuracy on multi-modal Named Entity Recognition datasets even without image information.

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Jingheng Ye, Yong Jiang, Xiaobin Wang, Yinghui Li, Yangning Li, Pengjun Xie, Fei Huang

Natural Language Interfaces

Challenge:	Recent advances in conversational information seeking (CIS) suggest a remedy for the lack of interactive clarification when people face unfamiliar domains.
Approach:	They propose a fully autonomous conversational information-seeking agent that couples large language models with a set of domain-specific tools to provide product demand clarification.
Outcome:	The proposed agent can iterate over 2,000 automatically generated sessions and score high on real-world evaluations without human annotation.

Graph Propagation based Data Augmentation for Named Entity Recognition (2023.acl-short)

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Jiong Cai, Shen Huang, Yong Jiang, Zeqi Tan, Pengjun Xie, Kewei Tu

Structure-Level Knowledge Distillation For Multilingual Sequence Labeling (2020.acl-main)

Challenge:	Synthetic data suffer from poor diversity, which leads to performance limitations.
Approach:	They propose a graph-propagated data augmentation framework for named entity recognition that uses graph propagation to build relationships between labeled data and unlabeled natural texts.
Outcome:	The proposed framework improves on a low-resource named entity recognition dataset.

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Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Fei Huang, Kewei Tu

Unsupervised Natural Language Parsing (Introductory Tutorial) (2021.eacl-tutorials)

Challenge:	Existing multilingual models still underperform individual monolingual models due to model capacity limitations.
Approach:	They propose to distill the structural knowledge of several monolingual models (teachers) to the unified multilingual model (student).
Outcome:	The proposed model outperforms strong baseline models and teacher models on 4 multilingual tasks with 25 datasets and has stronger zero-shot generalizability.

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Kewei Tu, Yong Jiang, Wenjuan Han, Yanpeng Zhao

WebAnchor: Anchoring Agent Planning to Stabilize Long-Horizon Web Reasoning (2026.findings-acl)

Challenge:	Unsupervised parsing learns a syntactic parser from training sentences without parse tree annotations.
Approach:	This tutorial will introduce what unsupervised parsing does and how it can be useful for and beyond syntactic parse.
Outcome:	This paper will provide an overview of major approaches to unsupervised parsing and analyze their strengths and weaknesses.

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Xinmiao Yu, Liwen Zhang, Xiaocheng Feng, Pengjun Xie, Jingren Zhou, Bing Qin, Yong Jiang

Enhancing Unsupervised Generative Dependency Parser with Contextual Information (P19-1)

Challenge:	Existing methods for reinforcement learning (RL)-based agents struggle with long-horizon planning and strategy coherence.
Approach:	They propose a reinforcement learning framework that decouples planning and execution.
Outcome:	The proposed framework outperforms baseline and first-step RL frameworks on four benchmarks.

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Wenjuan Han, Yong Jiang, Kewei Tu

Retrieved In-Context Principles from Previous Mistakes (2024.emnlp-main)

Challenge:	Existing approaches to unsupervised dependency parsing are based on probabilistic generative models that learn the joint distribution of the given sentence and its parse.
Approach:	They propose a probabilistic model that generates a sentence and its parse from a latent representation, which encodes global contextual information of the generated sentence.
Outcome:	The proposed model achieves competitive accuracy compared with state-of-the-art models.

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Hao Sun, Yong Jiang, Bo Wang, Yingyan Hou, Yan Zhang, Pengjun Xie, Fei Huang

MuVER: Improving First-Stage Entity Retrieval with Multi-View Entity Representations (2021.emnlp-main)

Challenge:	Recent advances in in-context learning (ICL) have limited customization and inadequate error coverage.
Approach:	They propose a method to retrieve in-context principles from mistakes to improve model performance.
Outcome:	The proposed framework enhances model performance when applied to various prompting strategies.

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Xinyin Ma, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Weiming Lu

Representation Learning

Challenge:	Recent advances in entity retrieval ignore the property that meanings of entity mentions diverge in different contexts and are related to various portions of descriptions.
Approach:	They propose a novel approach that constructs multi-view representations for entity descriptions and approximates the optimal view for mentions via a heuristic searching method.
Outcome:	The proposed approach achieves state-of-the-art performance on ZESHEL and improves quality of candidates on three standard Entity Linking datasets.

Let LLMs Take on the Latest Challenges! A Chinese Dynamic Question Answering Benchmark (2025.coling-main)

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Zhikun Xu, Yinghui Li, Ruixue Ding, Xinyu Wang, Boli Chen, Yong Jiang, Haitao Zheng, Wenlian Lu, Pengjun Xie, Fei Huang

Natural Language Interfaces

Challenge:	Recent work has noted that due to the extremely high cost of iterative updates of LLMs, they are often unable to answer dynamic questions well.
Approach:	They propose a Chinese Dynamic QA benchmark containing question-answer pairs related to the latest dynamic questions on the Chinese Internet.
Outcome:	The proposed benchmark will be one of the key data resources for improving LLMs’ Chinese question-answering ability in the future.

More Embeddings, Better Sequence Labelers? (2020.findings-emnlp)

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Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

A Survey of Unsupervised Dependency Parsing (2020.coling-main)

Challenge:	Existing work suggests contextual embeddings improve sequence labeling accuracy . but, there is no definite conclusion on whether concatenating different kinds of embeddables is effective .
Approach:	They propose a family of contextual embeddings that improves sequence labeling accuracy . they conduct extensive experiments on 3 tasks over 18 datasets and 8 languages .
Outcome:	The proposed family of contextual embeddings improves the accuracy of sequence labelers over non-contextual embedders.

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Wenjuan Han, Yong Jiang, Hwee Tou Ng, Kewei Tu

MANNER: A Variational Memory-Augmented Model for Cross Domain Few-Shot Named Entity Recognition (2023.acl-long)

Challenge:	Syntactic dependency parsing is an important task in natural language processing . unsupervised learning of dependency parses requires training sentences to be manually annotated with their correct parse trees.
Approach:	They propose to survey existing approaches to unsupervised dependency parsing . they identify two major classes of approaches and discuss recent trends .
Outcome:	The proposed methods can be used in semantic parsing, machine translation, relation extraction, and many other tasks.

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Jinyuan Fang, Xiaobin Wang, Zaiqiao Meng, Pengjun Xie, Fei Huang, Yong Jiang

Adversarial Attack and Defense of Structured Prediction Models (2020.emnlp-main)

Challenge:	Named Entity Recognition (NER) is a fundamental NLP task that aims at classifying mention spans into entity types.
Approach:	They propose a variational memory-augmented few-shot named entity recognition model that uses a memory module to store information from source domain and retrieve relevant information from the memory to augment few-shot task in target domain.
Outcome:	The proposed model can adapt the learned knowledge from source domain to target domain and achieve superior performance on English and Chinese cross domain few-shot NER datasets.

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Wenjuan Han, Liwen Zhang, Yong Jiang, Kewei Tu

Semantic Role Labeling as Dependency Parsing: Exploring Latent Tree Structures inside Arguments (2022.coling-1)

Challenge:	Existing approaches to building effective adversarial attackers focus on classification problems.
Approach:	They propose a framework that learns to attack a structured prediction model with feedbacks from multiple reference models.
Outcome:	The proposed framework is able to attack state-of-the-art models and boost them with training . it is based on a sequence-to-sequence model with feedbacks from multiple reference models .

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Yu Zhang, Qingrong Xia, Shilin Zhou, Yong Jiang, Guohong Fu, Min Zhang

Syntactic Text Processing

Challenge:	Recent works of SRL mainly fall into two lines: 1) BIO-based; 2) span-based.
Approach:	They propose to regard flat argument spans as latent subtrees, thus reducing SRL to a tree parsing task.
Outcome:	The proposed model performs better than previous syntax-agnostic models on CoNLL05 and CoNll12 benchmarks.

Do PLMs Know and Understand Ontological Knowledge? (2023.acl-long)

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Weiqi Wu, Chengyue Jiang, Yong Jiang, Pengjun Xie, Kewei Tu

BrowseConf: Confidence-Guided Test-Time Scaling for Web Agents (2026.findings-acl)

Challenge:	Existing studies on pretrained language models focus mainly on factual knowledge, lacking a systematic probing of ontological knowledge.
Approach:	They investigate whether Pretrained Language Models store ontological knowledge and have a semantic un- derstanding of the knowledge rather than rote memorization of the surface form.
Outcome:	The proposed models can memorize certain ontological knowledge and perform logical reasoning with given knowledge according to ontological entailment rules.

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Litu Ou, Kuan Li, Huifeng Yin, Liwen Zhang, Zhongwang Zhang, Xixi Wu, Rui Ye, Zile Qiao, Yong Jiang, Pengjun Xie, Fei Huang, Jingren Zhou

Challenge:	Existing work on confidence in LLMs is limited.
Approach:	They propose to use confidence scores to determine model answer quality and encourage model to try again until it reaches satisfactory confidence level.
Outcome:	The proposed methods significantly reduce token consumption while demonstrating competitive performance compared to baseline fixed budget methods.

An Investigation of Potential Function Designs for Neural CRF (2020.findings-emnlp)

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Zechuan Hu, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Representation Learning

Challenge:	Existing approaches to sequence labeling are based on the neural linear-chain CRF model.
Approach:	They propose a series of increasingly expressive potential functions for neural CRF models that integrate emission and transition functions and explicitly take contextual words as input.
Outcome:	The proposed model consistently achieves the best performance on the decomposed quadrilinear potential function based on the representations of two neighboring labels and two neighbored words.

Effective Demonstration Annotation for In-Context Learning via Language Model-Based Determinantal Point Process (2024.emnlp-main)

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Peng Wang, Xiaobin Wang, Chao Lou, Shengyu Mao, Pengjun Xie, Yong Jiang

Quantized Side Tuning: Fast and Memory-Efficient Tuning of Quantized Large Language Models (2024.acl-long)

Challenge:	Existing studies on large-scale labeled support sets are not feasible in practical scenarios.
Approach:	They introduce a language model-based determinant point process that considers uncertainty and diversity of unlabeled instances for optimal selection.
Outcome:	The proposed method can effectively select canonical examples on 9 NLU and 2 Generation datasets.

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Zhengxin Zhang, Dan Zhao, Xupeng Miao, Gabriele Oliaro, Zhihao Zhang, Qing Li, Yong Jiang, Zhihao Jia

Multi-view Classification Model for Knowledge Graph Completion (2020.aacl-main)

Challenge:	Existing methods to finetun large language models (LLMs) only update a small number of trainable parameters, or attempt to reduce the memory footprint during the training phase of the finetune process.
Approach:	They propose quantized side tuing (QST) which quantizes an LLM’s model weights into 4-bit to reduce the memory footprint of the original weights.
Outcome:	The proposed method reduces the memory footprint of the model weights, optimizer states, and intermediate activations while reducing the memory requirements.

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Wenbin Jiang, Mengfei Guo, Yufeng Chen, Ying Li, Jinan Xu, Yajuan Lyu, Yong Zhu

XLM-D: Decorate Cross-lingual Pre-training Model as Non-Autoregressive Neural Machine Translation (2022.emnlp-main)

Challenge:	Existing knowledge graph completion models only evaluated candidate triples from content information.
Approach:	They propose a multi-view classification model where multiple views are performed based on both content and context information for candidate triple evaluation.
Outcome:	The proposed model improves on two representative datasets and improves performance.

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Yong Wang, Shilin He, Guanhua Chen, Yun Chen, Daxin Jiang

Multilinguality

Challenge:	Existing pre-training language models have been successful in natural language understanding and autoregressive generation tasks, but non-autoregressive models have not been sufficiently successful.
Approach:	They propose a pre-trained masked language model (MLM) and a non-autoregressive generation model with a lightweight decorator.
Outcome:	The proposed model outperforms the previous mask-predict model on translation datasets by 19.9x.

Knowledge Mechanisms in Large Language Models: A Survey and Perspective (2024.findings-emnlp)

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Mengru Wang, Yunzhi Yao, Ziwen Xu, Shuofei Qiao, Shumin Deng, Peng Wang, Xiang Chen, Jia-Chen Gu, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

RaFe: Ranking Feedback Improves Query Rewriting for RAG (2024.findings-emnlp)

Challenge:	Using large language models, we can understand knowledge mechanisms in LLMs for learning, storage, utilization, and evolution.
Approach:	They propose to analyze knowledge mechanisms in Large Language Models (LLMs) they examine utilization, evolution, and the potential dark knowledge (hypothesis) they hope to help understand knowledge in LLMs and provide insights for future research .
Outcome:	The proposed model can be used to analyze the evolution of parametric knowledge in LLMs.

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Shengyu Mao, Yong Jiang, Boli Chen, Xiao Li, Peng Wang, Xinyu Wang, Pengjun Xie, Fei Huang, Huajun Chen, Ningyu Zhang

Query Routing for Homogeneous Tools: An Instantiation in the RAG Scenario (2024.findings-emnlp)

Challenge:	Large Language Models (LLMs) and Retrieval Augmentation Generation (RAG) techniques have evolved to enhance document retrieval by reformulating queries.
Approach:	They propose a framework for training query rewriting models that leverages a reranker framework.
Outcome:	The proposed framework provides ranking feedback aligned well with the rewriting objectives without needing signals from annotations and supports both online and offline training models.

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Feiteng Mu, Yong Jiang, Liwen Zhang, Liuchu Liuchu, Wenjie Li, Pengjun Xie, Fei Huang

Challenge:	Existing tool learning methods focus on selecting the most effective tool from a wide array of options, often overlooking cost-effectiveness.
Approach:	They propose to predict query performance and cost required to accomplish a given task . they then assign queries to the optimal tools in a cost-effective manner .
Outcome:	The proposed method achieves higher performance at lower cost compared to baseline approaches.

Robust Neural Machine Translation with Doubly Adversarial Inputs (P19-1)

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Yong Cheng, Lu Jiang, Wolfgang Macherey

SynWorld: Virtual Scenario Synthesis for Agentic Action Knowledge Refinement (2025.acl-short)

Challenge:	Neural machine translation (NMT) models suffer from noisy perturbations in the input . a gradient-based method to craft adversarial examples informed by the translation loss is proposed .
Approach:	They propose an approach to improve the robustness of NMT models by attacking the translation model with adversarial source examples and defending the model with a target input.
Outcome:	The proposed approach improves translation performance and robustness on clean inputs and higher on noisy data.

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Runnan Fang, Xiaobin Wang, Yuan Liang, Shuofei Qiao, Jialong Wu, Zekun Xi, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen

Challenge:	Using Large Language Models (LLMs)-based agents can enhance their understanding of environments and tasks.
Approach:	They propose a framework that allows agents to synthesize possible scenarios with multi-step action invocation within the action space and perform Monte Carlo Tree Search exploration to refine their action knowledge in the current environment.
Outcome:	The proposed framework synthesizes possible scenarios with multi-step action invocation within the action space and performs Monte Carlo Tree Search exploration to refine action knowledge in the current environment.

AIN: Fast and Accurate Sequence Labeling with Approximate Inference Network (2020.emnlp-main)

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Xinyu Wang, Yong Jiang, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu

Challenge:	Existing approaches to sequence labeling require sequential computation that makes parallelization impossible.
Approach:	They propose to employ a parallelizable approximate variational inference algorithm for the CRF model.
Outcome:	The proposed approach improves decoding speed and accuracy with long sentences and is parallelizable for faster training and prediction.

ContrastKV: Robust KV Cache Eviction via Contrastive Signal Fusion for Multi-Query Generalization (2026.acl-long)

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Xingchi Chen, Peiyuan Zong, Ziqiang Gao, Qing Li, Yong Jiang, Fa Zhu, Hui Li

QueryAttack: Jailbreaking Aligned Large Language Models Using Structured Non-natural Query Language (2025.findings-acl)

Challenge:	Existing query-agnostic approaches rely on a single proxy query, leading to fragile eviction decisions under high evict ratios.
Approach:	They propose a query-agnostic KV cache eviction algorithm that exploits complementary semantic and non-semantic signals.
Outcome:	Experiments show that the proposed algorithm outperforms state-of-the-art methods while retaining up to 92% accuracy with only 20% of the KV cache budget.

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Qingsong Zou, Jingyu Xiao, Qing Li, Zhi Yan, Yuhang Wang, Li Xu, Wenxuan Wang, Kuofeng Gao, Ruoyu Li, Yong Jiang

Modeling Label Correlations for Ultra-Fine Entity Typing with Neural Pairwise Conditional Random Field (2022.emnlp-main)

Challenge:	Existing methods to bypass security defenses of large language models (LLMs) are not effective, but QueryAttack can be jailbroken.
Approach:	They propose a framework to examine generalizability of safety alignment by translating malicious queries into structured non-natural query languages.
Outcome:	The proposed framework can achieve high attack success rates and jailbreak various defense methods on mainstream LLMs.

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Chengyue Jiang, Yong Jiang, Weiqi Wu, Pengjun Xie, Kewei Tu

DiningBench: A Hierarchical Multi-view Benchmark for Perception and Reasoning in the Dietary Domain (2026.acl-long)

Challenge:	Entity typing assigns semantic types to entities mentioned in text.
Approach:	They propose to use an undirected graphical model to formulate the UFET problem by combining unary potentials with a pairwise conditional random field model.
Outcome:	The proposed model outperforms the existing model with little cost and is thousands of times faster than the existing neural network module.

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Song Jin, Juntian Zhang, Xun Zhang, Zeying Tian, Fei Jiang, Guojun Yin, Wei Lin, Yong Liu, Rui Yan

Multimodality

Challenge:	Existing vision-language models lack fine-grained classification, single-view imagery, and inaccurate metadata.
Approach:	They propose a hierarchical, multi-view benchmark to evaluate VLMs across three levels of cognitive complexity.
Outcome:	The proposed benchmark evaluates vision-language models across three levels of complexity . it systematically identifies five primary failure modes . the proposed benchmarks are available on https://github.com/meituan/DiningBench.

Knowledge-Enhanced Named Entity Disambiguation for Short Text (2020.aacl-main)

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Zhifan Feng, Qi Wang, Wenbin Jiang, Yajuan Lyu, Yong Zhu

Knowledge Representation

Challenge:	Existing methods for named entity disambiguation are weak for short text . performance of existing methods drops dramatically for short texts .
Approach:	They propose a knowledge-enhanced method for named entity disambiguation . they use factual knowledge graph and conceptual knowledge graph to provide additional knowledge .
Outcome:	The proposed method achieves significant improvement on a large manually annotated short-text dataset and the state-of-the-art on three standard datasets.

EvoWiki: Evaluating LLMs on Evolving Knowledge (2025.acl-long)

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Wei Tang, Yixin Cao, Yang Deng, Jiahao Ying, Bo Wang, Yizhe Yang, Yuyue Zhao, Qi Zhang, Xuanjing Huang, Yu-Gang Jiang, Yong Liao

Challenge:	Existing knowledge evolution benchmarks are static and fail to capture the evolving nature of LLMs and knowledge.
Approach:	They propose an evolving dataset that categorizes information into stable, evolved, and uncharted states.
Outcome:	The proposed dataset is auto-updatable and enables evaluation of continuously changing knowledge and newly released LLMs.

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

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

Unfolding the Headline: Iterative Self-Questioning for News Retrieval and Timeline Summarization (2025.findings-naacl)

Challenge:	Existing pruning methods for Large Language Models rely on unstructured pruning or require special hardware to accelerate computation.
Approach:	They propose a retraining-free structured pruning method called SoBP . they evaluate the effectiveness of SoBP across 14 models from 3 LLM families .
Outcome:	The proposed method outperforms current state-of-the-art pruning methods on 8 datasets.

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Weiqi Wu, Shen Huang, Yong Jiang, Pengjun Xie, Fei Huang, Hai Zhao

Summarization

Challenge:	a new approach to timeline summarization is proposed for open-domain news content . large language models (LLMs) can be used to extract and organize news events from multiple documents .
Approach:	They propose a method to integrate Large Language Models into news timeline summarization by iterating on how events are linked and posing new questions.
Outcome:	The proposed system is able to generate and refresh chronological summaries based on documents retrieved in each round.

Improving Retrieval Augmented Open-Domain Question-Answering with Vectorized Contexts (2024.findings-acl)

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Zhuo Chen, Xinyu Wang, Yong Jiang, Pengjun Xie, Fei Huang, Kewei Tu

SpecCoT: Accelerating Chain-of-Thought Reasoning through Speculative Exploration (2025.findings-emnlp)

Challenge:	Retrieval Augmented Generation can be used to process long contexts in Open-Domain Question-Answering tasks.
Approach:	They propose a method to cover longer contexts in Open-Domain Question-Answering tasks by using a small encoder language model and cross-attention with origin inputs.
Outcome:	The proposed method can cover longer contexts while keeping the computing requirements close to the baseline.

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Junhan Shi, Yijia Zhu, Zhenning Shi, Dan Zhao, Qing Li, Yong Jiang

Benchmarking Temporal Reasoning and Alignment Across Chinese Dynasties (2026.eacl-short)

Challenge:	Large Reasoning Models suffer from high inference latency due to lengthy reasoning chains.
Approach:	They propose a collaborative framework that combines large and small models for effective reasoning.
Outcome:	The proposed framework reduces inference latency by 1.7-4.1 while maintaining comparable accuracy to standard large model inference.

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Zhenglin Wang, Jialong Wu, Pengfei Li, Yong Jiang, Deyu Zhou

Reasoning

Challenge:	Existing temporal reasoning benchmarks rely on rule-based construction and lack contextual depth . a recent study found existing LLMs struggle with nuanced temporal understanding .
Approach:	a benchmark is designed to evaluate LLMs on temporal reasoning in Chinese dynasties.
Outcome:	a new benchmark evaluates LLMs on temporal reasoning across Chinese dynasties . it emphasizes cross-entity relationships, pairwise temporal alignment, contextualized and culturally-grounded reasoning . results show existing LLM benchmarks struggle with nuanced temporal understanding .

FuseSearch: Learning Adaptive Parallel Execution for Efficient Code Localization (2026.findings-acl)

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Ke Xu, Siyang Xiao, Ming Liang, Yichen Yu, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li