Papers by Bo Huang

When Evolution Strategy Meets Language Models Tuning (2025.coling-main)

Challenge:	Recent studies have shown that Transformers is implicitly learning syntactic information from data, albeit is highly dependent on the quality and scale of the training data.
Approach:	They propose a syntax-guided localized self-attention model that allows directly incorporating grammar structures from an external constituency parser.
Outcome:	The proposed model improves translation performance on a variety of datasets, from small to large datasets and with different source languages.

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Bo Huang, Yuxin Jiang, Mingyang Chen, Yi Wang, Hongyang Chen, Wei Wang

EmbodiedBERT: Cognitively Informed Metaphor Detection Incorporating Sensorimotor Information (2024.findings-emnlp)

Challenge:	Autoregressive language models with pretraining often display limited capability in effectively following instructions.
Approach:	They propose an on-policy approach to optimize models by harnessing the principle of biological evolution, namely survival of the fittest.
Outcome:	The proposed method can achieve superior performance in various tasks and comparable performance in the human alignment task.

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Yu Li, Bo Peng, Yu-Yin Hsu, Chu-Ren Huang

From Adversarial Arms Race to Model-centric Evaluation: Motivating a Unified Automatic Robustness Evaluation Framework (2023.findings-acl)

Challenge:	Existing methods for metaphor detection rely on heuristics such as Metaphor Identification Procedure (MIP) and Selection Preference Violation (SPV).
Approach:	They propose a cognitively motivated module that leverages the cognitive information of embodiment that can be derived from word embeddings and explicitly models the process of sensorimotor change that has been demonstrated as essential for metaphor processing.
Outcome:	The proposed module can improve metaphor detection compared with the heuristic MIP that has been applied previously.

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Yangyi Chen, Hongcheng Gao, Ganqu Cui, Lifan Yuan, Dehan Kong, Hanlu Wu, Ning Shi, Bo Yuan, Longtao Huang, Hui Xue, Zhiyuan Liu, Maosong Sun, Heng Ji

Robustness in NLP

Challenge:	Existing models of robustness evaluation are incomprehensive, impractical, and invalid .
Approach:	They propose a framework for automatic robustness evaluation that shifts towards model-centric evaluation to further exploit the advantages of adversarial attacks.
Outcome:	The proposed framework is based on a model-centric evaluation protocol and a robustness evaluation protocol.

A Unified Span-Based Approach for Opinion Mining with Syntactic Constituents (2021.naacl-main)

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Qingrong Xia, Bo Zhang, Rui Wang, Zhenghua Li, Yue Zhang, Fei Huang, Luo Si, Min Zhang

XLM-E: Cross-lingual Language Model Pre-training via ELECTRA (2022.acl-long)

Challenge:	Existing methods for fine-grained opinion mining (OM) are based on span-based annotations, but they are not effective.
Approach:	They propose a unified span-based approach for the end-to-end OM setting using syntactic constituents and multi-task learning to integrate them into the proposed model.
Outcome:	The proposed approach achieves significant improvements over previous work on the MPQA 2.0 dataset and reduces the number of wrongly-predicted opinion expressions and roles.

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Zewen Chi, Shaohan Huang, Li Dong, Shuming Ma, Bo Zheng, Saksham Singhal, Payal Bajaj, Xia Song, Xian-Ling Mao, Heyan Huang, Furu Wei

CodeExp: Explanatory Code Document Generation (2022.findings-emnlp)

Challenge:	ELECTRA-style tasks are used to pretrain cross-lingual models for NLP tasks . masked language modeling tasks require massive computation resources, rendering such models quite expensive .
Approach:	They propose to use ELECTRA-style tasks to pre-train a cross-lingual language model . they propose to pretrain the model on multilingual and parallel corpora .
Outcome:	The proposed model outperforms baseline models on cross-lingual understanding tasks with much less computation cost.

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Haotian Cui, Chenglong Wang, Junjie Huang, Jeevana Priya Inala, Todd Mytkowicz, Bo Wang, Jianfeng Gao, Nan Duan

Explainability & Interpretability in NLP

Challenge:	Existing code-to-text generation models produce only high-level code summaries that do not capture implementation-level choices essential for these scenarios.
Approach:	They propose a code explanation generation task that uses code docstrings to refine models.
Outcome:	The proposed model can generate well-structured long docstrings comparable to human-written ones.

Re-ViLM: Retrieval-Augmented Visual Language Model for Zero and Few-Shot Image Captioning (2023.findings-emnlp)

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Zhuolin Yang, Wei Ping, Zihan Liu, Vijay Korthikanti, Weili Nie, De-An Huang, Linxi Fan, Zhiding Yu, Shiyi Lan, Bo Li, Mohammad Shoeybi, Ming-Yu Liu, Yuke Zhu, Bryan Catanzaro, Chaowei Xiao, Anima Anandkumar

Correctable-DST: Mitigating Historical Context Mismatch between Training and Inference for Improved Dialogue State Tracking (2022.emnlp-main)

Challenge:	Existing methods for image-to-text generation store all knowledge within parameters, thus requiring computational-expensive fine-tuning.
Approach:	They propose a Retrieval-augmented Visual Language Model that stores all the knowledge within parameters and can be used to retrieve it from the external database.
Outcome:	The proposed model significantly boosts performance for image-to-text generation tasks with 4x less parameters compared with baseline methods.

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Hongyan Xie, Haoxiang Su, Shuangyong Song, Hao Huang, Bo Zou, Kun Deng, Jianghua Lin, Zhihui Zhang, Xiaodong He

Be Helpful but Don’t Talk too Much - Enhancing Helpfulness in Conversations through Relevance in Multi-Turn Emotional Support (2024.emnlp-main)

Challenge:	Existing dialogue state tracking approaches predict the dialogue state of a target turn sequentially based on the ground-truth previous dialogue state.
Approach:	They propose a method that predicts dialogue state sequentially based on previous dialogue state . they propose generating a previously “predicted” dialogue state using ground-truth previous dialogue states .
Outcome:	The proposed method achieves 67.51%, 68.24%, 70.30%, 71.38%, and 81.27% joint goal accuracy on MultiWOZ 2.0-2.4 datasets.

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Junlin Li, Bo Peng, Yu-Yin Hsu, Chu-Ren Huang

Allocating Large Vocabulary Capacity for Cross-Lingual Language Model Pre-Training (2021.emnlp-main)

Challenge:	a helpful speaker should maintain an "effect-effort" tradeoff for a conversation to help and support . a study aimed to cultivate the awareness of "optimal relevance" into the cognitive process of conversation agents .
Approach:	They integrate the "Cognitive Relevance Principle" into emotional support agents . they found that the "relevance principle" is effective in generating human-like, helpful, harmless conversations .
Outcome:	The proposed method improves human-likedness and support in multi-turn conversations . the source code will be available at https://github.com/CN-Eyetk/VLESA-ORL.git .

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Bo Zheng, Li Dong, Shaohan Huang, Saksham Singhal, Wanxiang Che, Ting Liu, Xia Song, Furu Wei

Unifying Inference-Time Planning Language Generation (2026.findings-acl)

Challenge:	Existing models require a more expressive vocabulary to represent all languages . however, increasing the vocabulary size significantly slows down the pre-training speed .
Approach:	They propose an algorithm VoCap to determine the desired vocabulary capacity of each language.
Outcome:	The proposed algorithm improves cross-lingual model pre-training while reducing side effects of increasing vocabulary size.

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Prabhu Prakash Kagitha, Bo Sun, Ishan Desai, Andrew Zhu, Cassie Huang, Manling Li, Ziyang Li, Li Zhang

Text Generation

Challenge:	Large language models (LLMs) are used to generate a formal representation of a plan in a planning language.
Approach:	They propose a unifying organizational framework based on intermediate representations to unify the inference-time LLM-as-formalizer methodology for classical planning.
Outcome:	The proposed framework subsumes most existing work and proposes new ones that involve syntactically similar but high-resource intermediate languages.

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

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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.

NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts (2023.findings-acl)

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Yue Zhang, Bo Zhang, Haochen Jiang, Zhenghua Li, Chen Li, Fei Huang, Min Zhang

FinMaster: A Holistic Benchmark for Full-Pipeline Financial Management with Large Language Models (2026.findings-acl)

Challenge:	Recent studies on Chinese grammatical error correction focus on learning essays.
Approach:	They propose a Chinese grammatical error correction dataset that annotates multiple references for 12,500 sentences from three native domains.
Outcome:	The proposed dataset can be used to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains.

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Junzhe Jiang, Chang Yang, Aixin Cui, Sihan Jin, Yujing Zhang, Yilin Xiao, Ruiyu Wang, Bo Li, Xiao Huang, Danny Dongning Sun, Xinrun Wang

mPLUG-DocOwl 1.5: Unified Structure Learning for OCR-free Document Understanding (2024.findings-emnlp)

Challenge:	Existing benchmarks lack domain-specific data, realistic workflow-level task design, and standardized workflow- level evaluation.
Approach:	a new benchmark evaluates large language models on financial management workflows . the global financial services market is projected to grow to $37 trillion by 2027 .
Outcome:	a new benchmark for large language models on financial management workflows reveals critical capability gaps . accuracy drops from 90% on basic tasks to 40% on complex scenarios requiring multi-step reasoning . the global financial services market reached $25.8 trillion in 2022 and is projected to grow to $37 trillion by 2027 .

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Anwen Hu, Haiyang Xu, Jiabo Ye, Ming Yan, Liang Zhang, Bo Zhang, Ji Zhang, Qin Jin, Fei Huang, Jingren Zhou

Putting Captions to the Test: Evaluating Video Caption Quality through Multiple-Choice Question Answering (2026.acl-long)

Challenge:	Existing Multimodal Large Language Models lack general structure understanding abilities for text-rich document images.
Approach:	They propose to use unified structure learning to boost the performance of MLLMs by encoding structure information into text-rich images.
Outcome:	The proposed model achieves state-of-the-art on 10 visual document understanding benchmarks.

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Zizhen Wang, Bo Feng, Zhengfeng Lai, Shiyu Li, Yang Lu, Meng Cao, Ping Huang, Xiaoming Simon Wang

CR-GIS: Improving Conversational Recommendation via Goal-aware Interest Sequence Modeling (2022.coling-1)

Challenge:	Existing metrics for video captioning are based on text-based comparisons with ground-truth references.
Approach:	They propose a reference-free benchmark that assesses video captions based on their utility . they will release the benchmark to facilitate reproducible research .
Outcome:	The proposed benchmark improves on human-verified, fine-grained questions . it correlates significantly better with human judgments than existing metrics .

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Jinfeng Zhou, Bo Wang, Zhitong Yang, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

DISC: Plug-and-Play Decoding Intervention with Similarity of Characters for Chinese Spelling Check (2025.acl-long)

Challenge:	Existing methods to determine a goal item by sequentially tracking users’ interests ignore the rich goal-aware implicit interest sequence patterns in a dialog.
Approach:	They propose to model goal-aware implicit user interest sequence patterns in a dialog and a hierarchical Star Transformer to guide multi-turn utterances generation.
Outcome:	The proposed framework achieves more accurate recommendations with more fluent and coherent dialog utterances.

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Ziheng Qiao, Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang

GSID: Generative Semantic Indexing for E-Commerce Product Understanding (2025.emnlp-industry)

Challenge:	Chinese spelling check (CSC) tasks require that incorrect characters are usually similar to the correct ones in either phonetics or glyph.
Approach:	They propose a plug-and-play decoding intervention with similarity of characters module for Chinese spelling check (CSC) they propose to incorporate phonetic and glyph similarities only during the inference phase.
Outcome:	The proposed method significantly improves Chinese spelling check models on benchmarks and on benchmark datasets.

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Haiyang Yang, Qinye Xie, Qingheng Zhang, Chen Li Yu, Huike Zou, Chengbao Lian, Shuguang Han, Fei Huang, Jufeng Chen, Bo Zheng

Information Retrieval

Challenge:	Structured product information is a major bottleneck for the efficiency of e-commerce platforms.
Approach:	They propose a data-driven approach to generate product structured representations using product metadata.
Outcome:	Extensive experiments show that GSID can generate better product representations on real-world e-commerce platforms.

MAGRET: Machine-generated Text Detection with Rewritten Texts (2025.coling-main)

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Yifei Huang, Jiuxin Cao, Hanyu Luo, Xin Guan, Bo Liu

A Middle Path for On-Premises LLM Deployment: Preserving Privacy Without Sacrificing Model Confidentiality (2025.emnlp-main)

Challenge:	Existing studies focus on detecting machine-generated text in open-source models, but their performance on closed-source large models is limited.
Approach:	They propose a method to detect rewritten text from large language models using a BERT encoder and propose to refine it to achieve semantic alignment.
Outcome:	The proposed method outperforms baseline methods on three text-generated datasets.

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Hanbo Huang, Yihan Li, Bowen Jiang, Bo Jiang, Lin Liu, Zhuotao Liu, Ruoyu Sun, Shiyu Liang

EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain (2026.acl-long)

Challenge:	Privacy-sensitive users require deploying large language models within their own infrastructure (on-premises) vulnerabilities in local environments can lead to unauthorized access and potential model theft.
Approach:	They propose a framework that secures a few bottom layers in a secure environment . they propose metric to optimize trade-off between protection and customization flexibility .
Outcome:	The proposed framework outperforms baselines on five models with 1.3B to 70B parameters.

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Yi-Fan Lu, Xian-Ling Mao, Bo Wang, Xiao Liu, Heyan Huang

MMDEND: Dendrite-Inspired Multi-Branch Multi-Compartment Parallel Spiking Neuron for Sequence Modeling (2025.acl-long)

Challenge:	Extensive event extraction research has been conducted in many domains, including news, finance, and biology.
Approach:	They propose an end-to-end scientific event extraction framework for encoding nuggets into a grid matrix and simplifying complex event extraction as a nuggot-based grid modeling task.
Outcome:	The proposed framework performs well in scientific domain, demonstrating state-of-the-art performance.

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Kexin Wang, Yuhong Chou, Di Shang, Shijie Mei, Jiahong Zhang, Yanbin Huang, Man Yao, Bo Xu, Guoqi Li

Green & Sustainable NLP

Challenge:	Vanilla spiking neurons are simplified from complex biological neurons with dendrites, soma, and synapses into single somatic compartments.
Approach:	They propose a multi-branch, multi-compartment parallel spiking dendritic neuron with a proportion-adjustable multi-branched structure that enables long-term temporal dependencies.
Outcome:	The proposed model achieves better long-sequence modeling capability with fewer parameters and lower energy consumption.

EDDA: An Encoder-Decoder Data Augmentation Framework for Zero-Shot Stance Detection (2024.lrec-main)

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Daijun Ding, Li Dong, Zhichao Huang, Guangning Xu, Xu Huang, Bo Liu, Liwen Jing, Bowen Zhang

Social Welfare Function Leaderboard: On the Emergence of LLM Agents as the Welfare Dictator (2026.findings-acl)

Challenge:	Existing methods for enhancing text or data are limited by lack of logical connections between generated texts and training data.
Approach:	They propose an encoder-decoder data augmentation framework that combines large language models and chain-of-thought prompting to summarize texts into target-specific if-then rationales, establishing logical relationships.
Outcome:	The proposed framework significantly improves over state-of-the-art methods on benchmark datasets while enabling interpretable rationale-based learning.

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Zhengliang Shi, Ruotian Ma, Jen-tse Huang, Xinbei Ma, Xingyu Chen, Mengru Wang, Qu Yang, Yue Wang, Fanghua Ye, Ziyang Chen, Shanyi Wang, Cixing LI, Wenxuan Wang, Zhaopeng Tu, Xiaolong Li, Zhaochun Ren, Liefeng Bo

Towards Provably Secure Generative AI: Reliable Consensus Sampling (2026.findings-acl)

Challenge:	Large language models (LLMs) are increasingly entrusted with high-stakes decisions that affect human welfare.
Approach:	They evaluate 20 state-of-the-art Large language models (LLMs) and 20 LLM dictators to create a social welfare function benchmark.
Outcome:	The proposed model creates dilemma between maximizing collective efficiency and ensuring distributive fairness.

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Yu Cui, Hang Fu, Sicheng Pan, Zhuoyu Sun, Yifei Liu, Yuhong Nie, Bo Ran, Baohan Huang, Xufeng Zhang, Haibin Zhang, Cong Zuo, Licheng Wang

Robustness in NLP

Challenge:	Existing research on generative AI security is driven by mutually reinforcing attack and defense methodologies grounded in empirical experience.
Approach:	They propose a new algorithm that uses a random sampling algorithm to control risk.
Outcome:	The proposed algorithm improves robustness and utility while maintaining latency comparable to existing algorithms.

Attribution-Based Analysis and Optimization of Modular Agentic Workflows (2026.findings-acl)

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Yingxuan Yang, Bo Huang, Siyuan Qi, Chao Feng, Haoyi Hu, Yuxuan Zhu, Jinbo Hu, Haoran Zhao, Ziyi He, Xiao Liu, ZongYu Wang, Muning Wen, Lin Qiu, Xuezhi Cao, Xunliang Cai, Yong Yu, Weinan Zhang

Challenge:	Large Language Models (LLMs) have driven the rise of agentic workflows . yet, how can we attribute performance gains to individual upgrades and their interactions?
Approach:	They propose a game-theoretic framework that models component upgrades as players and evaluates component coalitions to compute Shapley values.
Outcome:	The proposed framework provides interaction-aware attribution and recommendation for model allocation under a fixed workflow structure.

Reinforcement Learning on Pre-Training Data (2026.acl-long)

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Siheng Li, Kejiao Li, Zenan Xu, Guanhua Huang, Kun Li, Haoyuan Wu, null Wujiajia, Zihao Zheng, Chenchen Zhang, Kun Shi, Xue Gong, Qi Yi, Ruibin Xiong, Tingqiang Xu, Yuhao Jiang, Jianfeng Yan, Yuyuan Zeng, Guanghui Xu, Jinbao Xue, Zhijiang xu, Zheng Fang, Shuai LI, Qibin Liu, Xiaoxue Li, Zhuoyu Li, Yangyu Tao, Fei Gao, Cheng Jiang, Bochao Wang, Kai Liu, Jianchen Zhu, Wai Lam, Bo Zhou, Di Wang

Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models (2025.acl-long)

Challenge:	Recent progress in large language models is driven by scaling of training compute through pre-training with nexttoken prediction (NTP) or post-training (RL) Pre-training using NTP enables models to acquire extensive knowledge and skills from general data, but it suffers from data inefficiency and catastrophic forgetting in continual learning settings.
Approach:	They propose to scale training compute through pre-training with next-token prediction (NTP) or post-training by scaling reinforcement learning (RL) to improve learning from general data.
Outcome:	Experiments on multiple benchmarks and models show that the proposed approach improves continual pre-training and provides a strong foundation for post-training on Qwen3-8B-Base.

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Zihan Qiu, Zeyu Huang, Bo Zheng, Kaiyue Wen, Zekun Wang, Rui Men, Ivan Titov, Dayiheng Liu, Jingren Zhou, Junyang Lin

Programming Languages in NLP

Challenge:	Existing Mixture-of-Experts training frameworks use a micro-batch to calculate LBL . micro-batches are restricted to a single sequence, preventing expert specialization .
Approach:	They propose to use a global-batch to loosen the load balance constraint for MoEs models . they propose to synchronize fi across micro-batches and then use it to calculate the LBL .
Outcome:	The proposed global-batch LBL improves the domain specialization of experts . the micro-battery LBL is almost at the sequence level, and the router is pushed to distribute the token evenly .

Enhancing Personalized Dialogue Generation with Contrastive Latent Variables: Combining Sparse and Dense Persona (2023.acl-long)

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Yihong Tang, Bo Wang, Miao Fang, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

TopKG: Target-oriented Dialog via Global Planning on Knowledge Graph (2022.coling-1)

Challenge:	Existing personalized dialogue agents model persona profiles from sparse or dense persona descriptions and dialogue histories.
Approach:	They propose a model that clusters dense persona descriptions into sparse categories and generates personalized responses from dialogue histories.
Outcome:	The proposed model improves on Chinese and English datasets.

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Zhitong Yang, Bo Wang, Jinfeng Zhou, Yue Tan, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

Diagram-Driven Course Questions Generation (2025.emnlp-main)

Challenge:	Existing target-oriented dialogs take a local and greedy strategy for response generation, where global planning is absent.
Approach:	They propose a global planning method for target-oriented dialog on a commonsense knowledge graph to adjust local response generation towards the global target.
Outcome:	The proposed method can reach the target with a higher success rate, fewer turns, and more coherent responses.

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Xinyu Zhang, Lingling Zhang, Yanrui Wu, Muye Huang, Wenjun Wu, Bo Li, Shaowei Wang, Basura Fernando, Jun Liu

Visual Data in NLP

Challenge:	Visual Question Generation (VQG) research focuses on natural images while neglecting diagrams, a critical component of educational materials.
Approach:	They propose a diagram-driven course questions generation task to generate diagram-relevant questions for specific courses.
Outcome:	The proposed framework outperforms existing models on DiagramQG while maintaining strong generalizability across natural image datasets.

Sensitivity-LoRA : Low-Load Sensitivity-Based Fine-Tuning for Large Language Models (2025.findings-emnlp)

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Hao Zhang, Bo Huang, Zhenjia Li, Xi Xiao, Hui Yi Leong, Zumeng Zhang, Xinwei Long, Tianyang Wang, Hao Xu

AMA: Adaptive Memory via Multi-Agent Collaboration (2026.findings-acl)

Challenge:	Low-Rank Adaptation (LoRA) is a promising approach to adapting LLMs to specialized tasks . existing rank allocation techniques remain computationally inefficient and unstable .
Approach:	They propose a low-rank adapted model that approximates model weight updates using low-ranked decomposition.
Outcome:	The proposed method is limited by its uniform rank allocation to each incremental matrix . it leverages the second-order derivatives of the loss function to capture weight sensitivity .

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Weiquan Huang, Zixuan Wang, Hehai Lin, Sudong Wang, Bo Xu, Qian Li, Beier Zhu, Linyi Yang, Chengwei Qin

Information Retrieval

Challenge:	Existing approaches to longterm memory rely on rigid retrieval granularity, accumulation-heavy maintenance strategies, and coarse-grained update mechanisms.
Approach:	They propose a framework that leverages coordinated agents to manage memory across multiple granularities.
Outcome:	The proposed framework outperforms state-of-the-art benchmarks while reducing token consumption by approximately 80%.

Multimodal Pretraining for Dense Video Captioning (2020.aacl-main)

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Gabriel Huang, Bo Pang, Zhenhai Zhu, Clara Rivera, Radu Soricut

Low-probability Tokens Sustain Exploration in Reinforcement Learning with Verifiable Reward (2026.findings-acl)

Challenge:	a billion hours of videos are being watched on YouTube every day . videos are difficult to skim through, making it harder to quickly target the relevant part(s) of a video.
Approach:	They propose to use a video timeline tag dataset to generate time-stamped annotations for videos . they propose to pretrain and finetune captioning models using YouCook2 and ViTT .
Outcome:	The proposed model generalizes well and is robust over a wide variety of instructional videos.

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Guanhua Huang, Tingqiang Xu, Mingze Wang, Qi Yi, Xue Gong, Siheng Li, Ruibin Xiong, Kejiao Li, Yuhao Jiang, Bo Zhou

Reasoning

Challenge:	Recent studies show that RLVR training is slow and results plateau as policy entropy collapses . low-probability regularization (Lp-Reg) reduces the number of low-quality exploratory tokens induced by RL training .
Approach:	They propose a method to reduce RLVR over-penalization by eliminating low-probability exploratory tokens . they propose 'Low-provability Regularization' to reduce the gradual elimination of low-quality exploratory entropy tokens.
Outcome:	The proposed method eliminates low-probability exploratory tokens and prevents suppression of potentially valuable low-property candidates.

Selective Prompting Tuning for Personalized Conversations with LLMs (2024.findings-acl)

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Qiushi Huang, Xubo Liu, Tom Ko, Bo Wu, Wenwu Wang, Yu Zhang, Lilian Tang

Low-Resource NLP

Challenge:	Personalization in conversational AI requires persona profiles and contextual understanding to create meaningful conversations.
Approach:	They propose a method that softly prompts LLMs for personalized conversations in a selective way.
Outcome:	The proposed approach improves response diversity by up to 90% on the CONVAI2 dataset.

LLMs-as-Instructors: Learning from Errors Toward Automating Model Improvement (2024.findings-emnlp)

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Jiahao Ying, Mingbao Lin, Yixin Cao, Wei Tang, Bo Wang, Qianru Sun, Xuanjing Huang, Shuicheng Yan

Challenge:	Using advanced Large Language Models, instructors can improve training of smaller models by analyzing their own model's errors.
Approach:	They propose a framework that leverages advanced Large Language Models to enhance training of smaller target models.
Outcome:	The proposed framework outperforms ChatGPT on multiple benchmarks and shows that it improves on both in-domain and out-of-domain benchmarks.

CASE: Aligning Coarse-to-Fine Cognition and Affection for Empathetic Response Generation (2023.acl-long)

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Jinfeng Zhou, Chujie Zheng, Bo Wang, Zheng Zhang, Minlie Huang

HyperAdaLoRA: Accelerating LoRA Rank Allocation During Training via Hypernetworks without Sacrificing Performance (2026.findings-acl)

Challenge:	Existing empathetic dialogue models only consider the affective aspect of empathy, which limits the capability of emotional response generation.
Approach:	They propose a model that aligns the user's cognition and affection at both the coarse-grained and fine-grounded levels and then automatically and manually evaluates the model.
Outcome:	The proposed model outperforms state-of-the-art models and generates more empathetic and informative responses.

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Hao Zhang, Zhenjia Li, Yifan Gao, Xi Xiao, Heng Zhang, Shuyang Zhang, null Xiaoxincc, Bo Huang, Yuhang Wu, Tianyang Wang, Hao Xu

Self-Supervised Sentence Polishing by Adding Engaging Modifiers (2023.acl-demo)

Challenge:	Low-Rank Adaptation (LoRA) assumes a uniform rank r for each incremental matrix, not accounting for the varying significance of weight matrices across modules and layers.
Approach:	They propose a framework that allows for faster convergence of low-rank adaptive models . they use a hypernetwork to prune the outputs of the hypernetworks to generate parameters .
Outcome:	The proposed framework accelerates convergence of AdaLoRA by leveraging a hypernetwork.

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Zhexin Zhang, Jian Guan, Xin Cui, Yu Ran, Bo Liu, Minlie Huang

Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Models (2024.acl-long)

Challenge:	a typical way to polish sentences is to add engaging modifiers, which enhance the meaning of the sentence.
Approach:	They propose a task that requires polishing sentences while maintaining fluency . they remove engaging modifiers from public resources and fine-tune LongLM to reconstruct original sentences from corrupted ones.
Outcome:	The proposed model generates more engaging sentences with suitable modifiers than strong baselines while keeping fluency.

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Xudong Lu, Qi Liu, Yuhui Xu, Aojun Zhou, Siyuan Huang, Bo Zhang, Junchi Yan, Hongsheng Li

Memorize Step by Step: Efficient Long-Context Prefilling with Incremental Memory and Decremental Chunk (2024.emnlp-main)

Challenge:	Mixture-of-Experts (MoE) LLMs achieve higher performance with fewer active parameters, but are still difficult to deploy due to their immense parameter sizes.
Approach:	They propose expert-level sparsification techniques to enhance the deployment efficiency of large language models by introducing plug-and-play expert pruning and skipping techniques.
Outcome:	The proposed methods reduce model sizes and increase inference speed while maintaining satisfactory performance across a wide range of tasks.

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Zhiyuan Zeng, Qipeng Guo, Xiaoran Liu, Zhangyue Yin, Wentao Shu, Mianqiu Huang, Bo Wang, Yunhua Zhou, Linlin Li, Qun Liu, Xipeng Qiu

Green & Sustainable NLP

Challenge:	Existing methods to optimize LLM for long sequences for long documents are slow and consume memory.
Approach:	They propose a method that starts with a small memory size and gradually increases it . they propose Decremental Chunk based on Incremental Memory (IMDC) which reduces chunk size while increasing memory size .
Outcome:	The proposed method is faster (1.45x) and reduces GPU memory consumption by 23.3% compared to fixed-size memory.

MoE-I2: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition (2024.findings-emnlp)

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Cheng Yang, Yang Sui, Jinqi Xiao, Lingyi Huang, Yu Gong, Yuanlin Duan, Wenqi Jia, Miao Yin, Yu Cheng, Bo Yuan

2D-DPO: Scaling Direct Preference Optimization with 2-Dimensional Supervision (2025.findings-naacl)

Challenge:	emergence of Mixture of Experts (MoE) LLMs has significantly advanced the development of language models.
Approach:	They propose a two-stage compression method tailored for Mixture of Experts to reduce the model size and decrease the computational cost.
Outcome:	The proposed method reduces model size and improves inference efficiency while maintaining performance in various zero-shot tasks.

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Shilong Li, Yancheng He, Hui Huang, Xingyuan Bu, Jiaheng Liu, Hangyu Guo, Weixun Wang, Jihao Gu, Wenbo Su, Bo Zheng

Challenge:	Existing methods that optimize for scalar scores or ranking reward ignore multi-dimensional nature of human preferences.
Approach:	They propose to extend the preference of Direct Preference Optimization to two dimensions: segments and aspects.
Outcome:	The proposed framework decomposes the overall objective into multi-segment and multi-aspect objectives.

Dynamic Prefix-Tuning for Generative Template-based Event Extraction (2022.acl-long)

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Xiao Liu, Heyan Huang, Ge Shi, Bo Wang

MuCGEC: a Multi-Reference Multi-Source Evaluation Dataset for Chinese Grammatical Error Correction (2022.naacl-main)

Challenge:	Experimental results show that our model achieves competitive results with the state-of-the-art classification-based model OneIE on ACE 2005.
Approach:	They propose a generative template-based event extraction method with dynamic prefix . they integrate context information with type-specific prefixes to learn a context-specific name for each context .
Outcome:	The proposed method achieves competitive results with state-of-the-art model OneIE on ACE 2005 and performs well on ERE.

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Yue Zhang, Zhenghua Li, Zuyi Bao, Jiacheng Li, Bo Zhang, Chen Li, Fei Huang, Min Zhang

Exploring Label Hierarchy in a Generative Way for Hierarchical Text Classification (2022.coling-1)

Challenge:	Using a multi-reference multi-source evaluation dataset, Chinese grammatical error correction (CGEC) is relatively scarce.
Approach:	They propose a multi-reference multi-source evaluation dataset for Chinese grammar error correction . the dataset contains 7,063 sentences written by Chinese-as-a-Second-Language learners .
Outcome:	The proposed dataset can be used to evaluate Chinese grammar errors in Chinese.

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Wei Huang, Chen Liu, Bo Xiao, Yihua Zhao, Zhaoming Pan, Zhimin Zhang, Xinyun Yang, Guiquan Liu

Text Classification

Challenge:	Existing methods for hierarchical text classification are lacking in the field of natural language processing.
Approach:	They propose a hierarchy-aware T5 model with path-adaptive attention mechanism to exploit hierarchical dependency across different levels.
Outcome:	The proposed model outperforms state-of-the-art models especially in Macro-F1 and low Macro.

OPeRA: A Dataset of Observation, Persona, Rationale, and Action for Evaluating LLMs on Human Online Shopping Behavior Simulation (2026.acl-long)

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Ziyi Wang, Yuxuan Lu, Wenbo Li, Amirali Amini, Bo Sun, Yakov Bart, Weimin Lyu, Jiri Gesi, Tian Wang, Jing Huang, Yu Su, Upol Ehsan, Malihe Alikhani, Toby Jia-Jun Li, Lydia Chilton, Dakuo Wang

Challenge:	evaluating LLMs' ability to mimic real user behavior remains an open challenge due to the lack of high-quality, publicly available datasets that capture both the observable actions and the internal reasoning of an actual user.
Approach:	They propose a dataset of Observation, Persona, Rationale, and Action collected from real human participants during online shopping sessions.
Outcome:	The proposed dataset is the first to evaluate how well current LLMs can accurately simulate the next web action of a specific user.

A Simple yet Effective Training-free Prompt-free Approach to Chinese Spelling Correction Based on Large Language Models (2024.emnlp-main)

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Houquan Zhou, Zhenghua Li, Bo Zhang, Chen Li, Shaopeng Lai, Ji Zhang, Fei Huang, Min Zhang

AIR: Complex Instruction Generation via Automatic Iterative Refinement (2025.emnlp-main)

Challenge:	Using an LLM for Chinese spelling correction tasks is completely different from previous approaches . given a Chinese character, there may exist many others with the same or similar pronunciations, or with similar shapes.
Approach:	They propose a training-free prompt-free approach to leverage large language models for Chinese spelling correction task.
Outcome:	The proposed model significantly improves performance on five public datasets, enabling them to compete with state-of-the-art domain-general CSC models.

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Wei Liu, Yancheng He, Yu Li, Hui Huang, Chengwei Hu, Jiaheng Liu, Shilong Li, Wenbo Su, Bo Zheng

Text Generation

Challenge:	Existing methods for generating complex instructions are resource-intensive and lack diversity.
Approach:	They propose a framework to generate complex instructions with constraints using a document-generated initial instruction and an iterative refinement framework to incorporate LLM-as-judge guidance.
Outcome:	The proposed framework significantly outperforms existing methods for generating complex instructions, and outperformed existing methods.

IGenBench: Benchmarking the Reliability of Text-to-Infographic Generation (2026.acl-long)

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Yinghao Tang, Xueding Liu, Boyuan Zhang, Tingfeng Lan, Yupeng Xie, Jiale Lao, Yiyao Wang, Haoxuan Li, Tingting Gao, Bo Pan, Luoxuan Weng, Xiuqi Huang, Minfeng Zhu, Yingchaojie Feng, Yuyu Luo, Wei Chen

MTGP: Multi-turn Target-oriented Dialogue Guided by Generative Global Path with Flexible Turns (2023.findings-acl)

Challenge:	Generated infographics may appear correct at first glance but contain easily overlooked issues, such as distorted data encoding or incorrect textual content.
Approach:	They propose to evaluate reliability of text-to-infographic generation using IGenBench . they employ multimodal large language models to verify each question .
Outcome:	The proposed framework decomposes reliability verification into atomic yes/no questions based on a taxonomy of 10 question types.

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Anqi Liu, Bo Wang, Yue Tan, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

Targeting the Needle, Ignoring the Haystack: Anchoring Crucial Cues for Evolving Scam Call Detection via an LLM-Assisted Classifier (2026.findings-acl)

Challenge:	Existing approaches focus on global planning, which plans toward the target before the conversation.
Approach:	They propose to generate a global path as a natural language sentence instead of a sequence of nodes.
Outcome:	The proposed method has fewer turns, more coherent semantics, and higher success rate than baselines.

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Tong Wu, Qinliang Su, Jianxing Yu, Bo Liang, Minhua Huang

Knowledge Representation Learning with Contrastive Completion Coding (2021.findings-emnlp)

Challenge:	Existing methods for fraud detection on online service platforms often fail to generalize due to the scarcity of labeled data and the continuous evolution of conversational contexts.
Approach:	They propose a framework that anchors detection on Semantic Primitives . they prioritize stable evidence over conversational noise to ensure a verifiable fraud tactic .
Outcome:	The proposed framework achieves superior robustness and efficiency compared to baselines . it prioritizes stable evidence over diverse conversational noise .

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Bo Ouyang, Wenbing Huang, Runfa Chen, Zhixing Tan, Yang Liu, Maosong Sun, Jihong Zhu

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

Challenge:	Existing knowledge representation learning methods suffer from immaturity on tackling potentially-imperfect knowledge graphs and highly-imbalanced positive-negative instances during training.
Approach:	They propose a framework for knowledge representation learning that incorporates two functional components to achieve robust embedding for each entity/relation.
Outcome:	The proposed framework achieves better convergence against state-of-the-art methods on several benchmarks.

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

Low-Resource NLP

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.

DCMKC: A Dual Consistency Matching Approach for Multi-hop Question Answering in LLMs (2025.findings-emnlp)

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Xinyi Wang, Yiping Song, Chang Liu, Tingjin Luo, Bo Liu, Zheng Xie, Minlie Huang

Structured Data in NLP

Challenge:	Existing reasoning based on chains of thought (CoTs) fails to find logical connections between reasoning steps .
Approach:	They propose a method to match KG reasoning chains with CoTs based on semantic similarity . they use a knowledge graph to find relevant information "within" each reasoning step .
Outcome:	The proposed method outperforms baselines on multi-answer questions with 5.1% improvement over baselines.

Do Vision-Language Models Have Internal World Models? Towards an Atomic Evaluation (2025.findings-acl)

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Qiyue Gao, Xinyu Pi, Kevin Liu, Junrong Chen, Ruolan Yang, Xinqi Huang, Xinyu Fang, Lu Sun, Gautham Kishore, Bo Ai, Stone Tao, Mengyang Liu, Jiaxi Yang, Chao-Jung Lai, Chuanyang Jin, Jiannan Xiang, Benhao Huang, Zeming Chen, David Danks, Hao Su, Tianmin Shu, Ziqiao Ma, Lianhui Qin, Zhiting Hu

MGPO: Thinking with Images via Multi-Turn Grounding-Based Reinforcement Learning (2026.findings-acl)

Challenge:	Recent studies have evaluated and shown limitations in specific capabilities such as visual understanding, but a systematic evaluation of VLMs’ fundamental WM abilities remains absent.
Approach:	They propose a framework that assesses perception and prediction to provide an atomic evaluation of VLMs as WMs.
Outcome:	The proposed framework assesses perception and prediction abilities on 15 latest VLMs and compares them to human-level models.

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Xinyu Huang, Yuhao Dong, Weiwei Tian, Bo Li, Rui Feng, Ziwei Liu

Text Editing as Imitation Game (2022.findings-emnlp)

Challenge:	State-of-the-art large multimodal models face challenges when processing high-resolution images, as these inputs are converted into enormous visual tokens, many of which are irrelevant to the downstream task.
Approach:	They propose a multi-turn grounding-based policy optimization framework that enables LMMs to iteratively focus on key visual regions by automatically cropping sub-images based on model-predicted grounding coordinates within a multiple-turn conversation framework.
Outcome:	The proposed framework improves on Qwen2.5-VL-7B with 21K samples and surpasses OpenAI’s o1 and GPT-4o models on the out-of-distribution (OOD) V* Bench.

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Ning Shi, Bin Tang, Bo Yuan, Longtao Huang, Yewen Pu, Jie Fu, Zhouhan Lin

On Domain-Adaptive Post-Training for Multimodal Large Language Models (2025.findings-emnlp)

Challenge:	Text editing is an important domain of processing tasks to edit the text in a localized fashion, such as text simplification.
Approach:	They propose a nonautoregressive decoder for state-to-action demonstrations that parallels the decoding while retaining the dependencies between tokens.
Outcome:	The proposed model outperforms the autoregressive baselines on a suite of Arithmetic Equation benchmarks in terms of performance, efficiency, and robustness.

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Daixuan Cheng, Shaohan Huang, Ziyu Zhu, Xintong Zhang, Xin Zhao, Zhongzhi Luan, Bo Dai, Zhenliang Zhang

Improving Seq2Seq Grammatical Error Correction via Decoding Interventions (2023.findings-emnlp)

Challenge:	Adapting general multimodal large language models to specific domains is important for practical applications.
Approach:	They investigate domain adaptation of multimodal large language models via post-training . they develop a generate-then-filter pipeline that curates diverse visual instruction tasks .
Outcome:	The proposed model outperforms existing models in domain adaptation by combining data from open-source models with training pipelines.

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Houquan Zhou, Yumeng Liu, Zhenghua Li, Min Zhang, Bo Zhang, Chen Li, Ji Zhang, Fei Huang

Boosting Event Extraction with Denoised Structure-to-Text Augmentation (2023.findings-acl)

Challenge:	Existing approaches to grammatical error correction (GEC) are sequence-to-sequence and sequence-edit.
Approach:	They propose a unified decoding intervention framework that employs an external critic to assess the appropriateness of the token to be generated incrementally.
Outcome:	The proposed framework outperforms baselines and state-of-the-art methods on English and Chinese datasets.

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Bo Wang, Heyan Huang, Xiaochi Wei, Ge Shi, Xiao Liu, Chong Feng, Tong Zhou, Shuaiqiang Wang, Dawei Yin

AEGIS: A Holistic Benchmark for Evaluating Forensic Analysis of AI-Generated Academic Images (2026.acl-long)

Challenge:	Existing methods for event extraction neglect grammatical incorrectness, structure misalignment, and semantic drifting . et al., 2004; Ahn, 2006) show that the proposed method generates more diverse text representations for event extracting compared with the state-of-the-art.
Approach:	They propose a framework for event extraction that generates additional training data and iteratively selects the effective subset from the generated training data.
Outcome:	The proposed method generates more diverse representations of training data and achieves comparable results with the state-of-the-art.

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Bo Zhang, Tzu-Yen Ma, Zichen Tang, Junpeng Ding, Zirui Wang, Yizhuo Zhao, Peilin Gao, Zijie Xi, Zixin Ding, Haiyang Sun, Haocheng Gao, Yuan Liu, Liangjia Wang, Yiling Huang, Yujie Wang, Yuyue Zhang, Ronghui Xi, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Haihong E

MRE-MI: A Multi-image Dataset for Multimodal Relation Extraction in Social Media Posts (2025.findings-naacl)

Challenge:	AEGIS examines whether current models can effectively audit AI-generated images in academic papers.
Approach:	They propose a holistic benchmark for forensic analysis of AI-Generated academic ImageS that reveals limitations in academic image forensics.
Outcome:	AEGIS compared with existing benchmarks on seven academic categories and features key advances in forensic analysis.

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Shizhou Huang, Bo Xu, Changqun Li, Yang Yu, Xin Alex Lin

EchoMLLM: Incentivizing Echocardiographic Video Understanding with Keyframe Grounding and Report Generation (2026.findings-acl)

Challenge:	Existing approaches to Multimodal Relation Extraction focus on single image scenarios . current approaches focus on text paired with a single image, ignoring valuable insights provided by remaining images.
Approach:	They propose a human-annotated dataset that includes multi-image and single-image instances for relation extraction.
Outcome:	The proposed model significantly improves relation extraction in multi-image scenarios.

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Heyu Huang, Wanran Sun, Chi Chen, Bo Chen, Zonghao Guo, Yuhua Li, Ruixuan Li, Kunlun He, Maosong Sun

Visual Data in NLP

Challenge:	Echocardiography analysis requires a dual capability: rigorous quantitative keyframe localization and comprehensive qualitative synthesis.
Approach:	They propose a unified framework designed for real-world echocardiography video understanding.
Outcome:	a new framework is designed to support real-world echocardiography video understanding . it reduces temporal grounding errors by up to 76% and improves report generation quality by 65% .

Asking the Crowd: Question Analysis, Evaluation and Generation for Open Discussion on Online Forums (P19-1)

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Zi Chai, Xinyu Xing, Xiaojun Wan, Bo Huang

Text Generation

Challenge:	Existing work on teaching machines to ask questions focused on generating fixed answers.
Approach:	They propose a model to generate open-answered questions from real-world news for open discussion . they analyze how language use affects the number of answers .
Outcome:	The proposed model generates questions with higher quality than most text generation methods.

RTCFake: Speech Deepfake Detection in Real-Time Communication (2026.findings-acl)

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Jun Xue, Zhuolin Yi, Yihuan Huang, Yanzhen Ren, Yujie Chen, Cunhang Fan, Zicheng Su, Yongcheng Zhang, Bo Cai

Chinese SimpleQA: A Chinese Factuality Evaluation for Large Language Models (2025.acl-long)

Challenge:	Existing detection studies focus on offline simulations and struggle to cope with complex distortions introduced during RTC transmission.
Approach:	They propose a large-scale speech deepfake dataset tailored for RTC scenarios . the dataset is constructed by transmitting speech through multiple social media and conferencing platforms .
Outcome:	The proposed dataset is constructed by transmitting speech through multiple mainstream social media and conferencing platforms, enabling precise pairing between offline and online speech.

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Yancheng He, Shilong Li, Jiaheng Liu, Yingshui Tan, Weixun Wang, Hui Huang, Xingyuan Bu, Hangyu Guo, Chengwei Hu, Boren Zheng, Zhuoran Lin, Dekai Sun, Zhicheng Zheng, Wenbo Su, Bo Zheng

Facilitating Multi-turn Emotional Support Conversation with Positive Emotion Elicitation: A Reinforcement Learning Approach (2023.acl-long)

Challenge:	Current frontier models sometimes generate false outputs or answers that are not substantiated by evidence.
Approach:	They propose Chinese SimpleQA, a Chinese benchmark to evaluate LLMs' factuality . they focus on Chinese language over 6 major topics with 99 diverse subtopics .
Outcome:	The Chinese SimpleQA benchmark evaluates the factuality ability of LLMs . the questions and answers are short and easy-to-evaluate .

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Jinfeng Zhou, Zhuang Chen, Bo Wang, Minlie Huang

PathwiseRAG: Multi-Dimensional Exploration and Integration Framework (2025.emnlp-main)

Challenge:	Existing approaches to provide emotional support (ESC) ignore the effect on ES and lack explicit goals to guide emotional positive transition.
Approach:	They propose a new paradigm to formalize multi-turn ESC as a process of positive emotion elicitation.
Outcome:	The proposed model outperforms existing models in achieving positive emotion elicitation while maintaining conversational goals like coherence.

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Hengrui Zhang, Pin-Siang Huang, Zhen Zhang, Peican Lin, Yao-Ching Yu, Bo Hu, Yulu Du

Information Retrieval

Challenge:	Existing retrieval-augmented generation systems employ rigid retrieval strategies . static retrieval produces knowledge blind spots, missing connections between quantum algorithms and encryption vulnerabilities .
Approach:	PathwiseRAG addresses these challenges through intent-aware strategy selection . it constructs a directed acyclic graph of interconnected sub-problems and explores multiple reasoning trajectories .
Outcome:	The proposed framework achieves higher accuracy and better reliability than current systems.

Consistency Regularization for Cross-Lingual Fine-Tuning (2021.acl-long)

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Bo Zheng, Li Dong, Shaohan Huang, Wenhui Wang, Zewen Chi, Saksham Singhal, Wanxiang Che, Ting Liu, Xia Song, Furu Wei

Multilinguality

Challenge:	Experimental results show that consistency regularization improves cross-lingual fine-tuning . pre-trained cross-linguistic models can transfer task-specific supervision from one language to the other .
Approach:	They propose to improve cross-lingual fine-tuning with consistency regularization . they use example consistency regularized to penalize prediction sensitivity to four types of data augmentations .
Outcome:	The proposed method improves cross-lingual fine-tuning across tasks . it can be generalized to other target languages without additional training .

MNER-MI: A Multi-image Dataset for Multimodal Named Entity Recognition in Social Media (2024.lrec-main)

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Shizhou Huang, Bo Xu, Changqun Li, Jiabo Ye, Xin Lin

Improving Pretrained Cross-Lingual Language Models via Self-Labeled Word Alignment (2021.acl-long)

Challenge:	Recent research has focused on multimodal named entity recognition (MNER) but current approaches focus on text and a single accompanying image, leaving a significant research gap in multi-image scenarios.
Approach:	They propose to construct a human-annotated MNER dataset with multiple images called MNER-MI and a temporal prompt model with multiple image to address the new challenges in multi-image scenarios.
Outcome:	The proposed method achieves state-of-the-art results on both MNER-MI and MNER -MI-Plus, demonstrating its effectiveness.

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Zewen Chi, Li Dong, Bo Zheng, Shaohan Huang, Xian-Ling Mao, Heyan Huang, Furu Wei

IDEA: An Interpretable and Editable Decision-Making Framework for LLMs via Verbal-to-Numeric Calibration (2026.findings-acl)

Challenge:	Experimental results show that denoising word alignment improves cross-lingual transferability . most applications and resources are still English-centric, making non-English users hard to access.
Approach:	They propose to denoise word alignment as a cross-lingual pre-training task . they first self-label word alignments for parallel sentences and then mask tokens .
Outcome:	The proposed model improves cross-lingual transferability on token-level tasks, especially on question answering, and structured prediction.

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Yanji He, Yuxin Jiang, Yiwen Wu, Bo Huang, Jiaheng Wei, Wei Wang

Explainability & Interpretability in NLP

Challenge:	Existing approaches fail to integrate domain expert insights beyond simple prompting.
Approach:	They propose a framework that extracts LLM decision knowledge into an interpretable parametric model over semantically meaningful factors.
Outcome:	Experiments show that IDEA outperforms DeepSeek R1 and GPT-5.2 in accuracy and accuracy.

Different Data, Different Modalities! Reinforced Data Splitting for Effective Multimodal Information Extraction from Social Media Posts (2022.coling-1)

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Bo Xu, Shizhou Huang, Ming Du, Hongya Wang, Hui Song, Chaofeng Sha, Yanghua Xiao

Guiding Dialogue Agents to Complex Semantic Targets by Dynamically Completing Knowledge Graph (2023.findings-acl)

Challenge:	Recent multimodal information extraction approaches overestimate the significance of images.
Approach:	They propose a general data splitting strategy to divide social media posts into two sets to achieve better performance under information extraction models of the corresponding modalities.
Outcome:	The proposed method outperforms existing models on two different multimodal information extraction tasks.

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Yue Tan, Bo Wang, Anqi Liu, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

Intention Knowledge Graph Construction for User Intention Relation Modeling (2026.eacl-long)

Challenge:	Existing knowledge graphs are incomplete in tracking complex semantic relations of the target-oriented dialogue.
Approach:	They combine methods of knowledge retrieval and relationship prediction to construct a context-related dynamic KG and a metric to evaluate the tracked path automatically.
Outcome:	The proposed method can control the agent more logically and smoothly toward the complex target.

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Jiaxin Bai, Zhaobo Wang, Junfei Cheng, Dan Yu, Zerui Huang, Weiqi Wang, Xin Liu, Chen Luo, Yanming Zhu, Bo Li, Yangqiu Song

CoCGAN: Contrastive Learning for Adversarial Category Text Generation (2022.coling-1)

Challenge:	Existing knowledge graphs focus on connecting intentions but lacks the ability to model the relationships between different intentions.
Approach:	They propose a framework to automatically generate an intention knowledge graph, capturing connections between user intentions.
Outcome:	The proposed model outperforms state-of-the-art methods and shows its utility.

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Xin Sheng, Linli Xu, Yinlong Xu, Changcun Bao, Huang Chen, Bo Ren

QRMeM: Unleash the Length Limitation through Question then Reflection Memory Mechanism (2024.findings-emnlp)

Challenge:	Experimental results on synthetic and real category text generation datasets demonstrate that CoCGAN can achieve significant improvements over the baseline category text generators.
Approach:	They propose to incorporate contrastive learning into adversarial category text generation by using a discriminator to optimize a contrastive learn objective to capture more flexible data-to-class relations and data- to-data relations among training samples.
Outcome:	The proposed model improves on synthetic and real category text generation datasets.

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Bo Wang, Heyan Huang, Yixin Cao, Jiahao Ying, Wei Tang, Chong Feng

Question Answering

Challenge:	Existing methods for processing large textual content face insufficient adaptation to task-specific needs and missing multi-segmentation relationships.
Approach:	They propose a question then reflection memory mechanism which integrates a dual-structured memory pool and a structured graph guidance to facilitate a reflective trial-and-error approach for navigating and identifying relevant segments.
Outcome:	The proposed model achieves superior performance on multiple-choice questions and multi-doc QA.

Aligning Recommendation and Conversation via Dual Imitation (2022.emnlp-main)

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Jinfeng Zhou, Bo Wang, Minlie Huang, Dongming Zhao, Kun Huang, Ruifang He, Yuexian Hou

CodeV: Issue Resolving with Visual Data (2025.findings-acl)

Challenge:	Existing conversational recommendation systems ignore the advantage of user interest shift in connecting recommendation and conversation, leading to an ineffective loose coupling structure.
Approach:	They propose a dual imitation to explicitly align recommendation and conversation paths . they propose to generate high-quality responses with accurate recommendations and coherent explanations .
Outcome:	The proposed model outperforms the state-of-the-art models on recommendation and conversation performance with automatic, human, and novel explainability metrics.

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Linhao Zhang, Daoguang Zan, Quanshun Yang, Zhirong Huang, Dong Chen, Bo Shen, Tianyu Liu, Yongshun Gong, Huang Pengjie, Xudong Lu, Guangtai Liang, Lizhen Cui, Qianxiang Wang

LIME: Less Is More for MLLM Evaluation (2025.findings-acl)

Challenge:	Large Language Models (LLMs) have expanded to more complex repository-level tasks.
Approach:	They propose a first approach to leveraging visual data to enhance the issue-resolving capabilities of Large Language Models (LLMs) they demonstrate the effectiveness of CodeV and provide valuable insights into leveraging visualization to resolve GitHub issues.
Outcome:	The proposed approach improves the issue-resolving capabilities of Large Language Models (LLMs) by using visual data.

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King Zhu, Qianbo Zang, Shian Jia, Siwei Wu, Feiteng Fang, Yizhi Li, Shuyue Guo, Tianyu Zheng, Jiawei Guo, Bo Li, Haoning Wu, Xingwei Qu, Jian Yang, Ruibo Liu, Xiang Yue, Jiaheng Liu, Chenghua Lin, Hamid Alinejad-Rokny, Min Yang, Shiwen Ni, Wenhao Huang, Ge Zhang