Papers by Li Miao

WhyAct: Identifying Action Reasons in Lifestyle Vlogs (2021.emnlp-main)

Challenge:	Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective in enhancing LLMs’ short-context reasoning but falters in long-contemporal scenarios requiring precise grounding and multi-hop reasoning.
Approach:	They propose a framework that constructs high-difficulty, multi-hop long-context QA pairs with inherent reasoning chains to overcome this bottleneck.
Outcome:	The proposed framework outperforms RLVR baselines and matches frontier LLMs while using far fewer parameters.

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Oana Ignat, Santiago Castro, Hanwen Miao, Weiji Li, Rada Mihalcea

Penetrating Linguistic Disguises: A Slang-aware Label-Aligned Framework for Fine-Grained Toxicity Extraction in Chinese Hate Speech Detection (2026.findings-acl)

Challenge:	Existing systems for action recognition rely on pattern memorization and do not understand the action.
Approach:	They propose a multimodal model that leverages visual and textual information to automatically infer the reasons corresponding to an action presented in the video.
Outcome:	The proposed model leverages visual and textual information to automatically infer the reasons corresponding to an action presented in the video.

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Wei Liu, Xiaoliang Chen, Duoqian Miao, Xu Gu, Xianyong Li, Yajun Du

Latent-Optimized Adversarial Neural Transfer for Sarcasm Detection (2021.naacl-main)

Challenge:	Flexible word boundaries and linguistic obfuscation, particularly slang, challenge precise span-level hate speech detection in Chinese.
Approach:	They propose a Slang-aware Label-Aligned Framework that maps slang to explicit hate semantics and uses task-specific branches to mitigate feature interference.
Outcome:	The proposed framework reduces ambiguity by mapping obscure slang to explicit hate semantics.

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Xu Guo, Boyang Li, Han Yu, Chunyan Miao

Sentiment Analysis

Challenge:	Existing datasets for sarcasm detection are limited due to the difficulty in acquiring ground-truth annotations.
Approach:	They propose a generalized latent optimization strategy that allows different losses to accommodate each other and improves training dynamics.
Outcome:	The proposed approach outperforms transfer learning and meta-learning baselines and achieves 10.02% performance gain on the iSarcasm dataset.

Word Matters: What Influences Domain Adaptation in Summarization? (2024.acl-long)

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Yinghao Li, Siyu Miao, Heyan Huang, Yang Gao

Text Generation

Challenge:	Large Language Models (LLMs) can generalize domain datasets unseen during training but are not able to predict domain adaptation performance.
Approach:	They propose to quantify dataset learning difficulty as the learning difficulty of generative summarization, which is determined by word-based compression rate and abstraction level.
Outcome:	The proposed model can predict performance on unknown domain datasets without training, and it is based on the findings.

FB-Bench: A Fine-Grained Multi-Task Benchmark for Evaluating LLMs’ Responsiveness to Human Feedback (2025.emnlp-main)

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Youquan Li, Miao Zheng, Fan Yang, Guosheng Dong, Bin Cui, Weipeng Chen, Zenan Zhou, Wentao Zhang

Aligning VLM Assistants with Personalized Situated Cognition (2025.acl-long)

Challenge:	Existing research focuses on benchmarking LLMs in single-turn dialogues, neglecting the nuanced nature of human feedback within real-world usage scenarios.
Approach:	They propose a fine-grained, multi-task benchmark designed to evaluate LLMs’ responsiveness to human feedback under real-world usage scenarios in Chinese.
Outcome:	The proposed benchmarks show that human feedback can significantly impact LLMs’ responsiveness in real-world usage scenarios.

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Yongqi Li, Shen Zhou, Xiaohu Li, Xin Miao, Jintao Wen, Mayi Xu, Jianhao Chen, Birong Pan, Hankun Kang, Yuanyuan Zhu, Ming Zhong, Tieyun Qian

End-to-End Modeling via Information Tree for One-Shot Natural Language Spatial Video Grounding (2022.acl-long)

Challenge:	Existing studies on vision-language models aligned with general human objectives have not been successful because people with diversified backgrounds have different cognition even in the same situation.
Approach:	They propose to characterize individuals based on the sociological concept of Role-Set and then evaluate their actions to see whether personalized alignment is achieved.
Outcome:	The proposed framework constructs a cognition-aware and action-based reward model for personalized alignment.

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Mengze Li, Tianbao Wang, Haoyu Zhang, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Wenming Tan, Jin Wang, Peng Wang, Shiliang Pu, Fei Wu

CodeArena: Evaluating and Aligning CodeLLMs on Human Preference (2025.emnlp-main)

Challenge:	Existing methods for grounding video frames with dense annotations require enormous amount of human effort.
Approach:	They propose to ground natural language in video frames with only one frame labeled . they propose an end-to-end model that eliminates interference of irrelevant frames .
Outcome:	The proposed model can ground natural language in all video frames with only one frame labeled . the proposed model eliminates interference of irrelevant frames based on branch search and cropping techniques .

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Jian Yang, Jiaxi Yang, Wei Zhang, Jin Ke, Yibo Miao, Lei Zhang, Liqun Yang, Zeyu Cui, Yichang Zhang, Zhoujun Li, Binyuan Hui, Junyang Lin

Towards Robust Neural Machine Translation with Iterative Scheduled Data-Switch Training (2022.coling-1)

Challenge:	Code large language models (codeLLMs) focus on synthesizing the correct code snippet, ignoring the alignment with human preferences.
Approach:	They propose a benchmark code-based on 40 categories and 44 programming languages to emulate real-world coding tasks.
Outcome:	The proposed benchmarks show that open-source code LLMs perform better than open-sourced ones.

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Zhongjian Miao, Xiang Li, Liyan Kang, Wen Zhang, Chulun Zhou, Yidong Chen, Bin Wang, Min Zhang, Jinsong Su

Robustness in NLP

Challenge:	Existing methods on robust neural machine translation (NMT) construct adversarial examples by injecting noise into authentic examples and indiscriminately exploit two types of examples.
Approach:	They propose an iterative scheduled data-switch training framework to mitigate this problem by injecting noise into authentic examples and indiscriminately exploiting two types of examples.
Outcome:	The proposed model outperforms several competitive benchmarks on four translation benchmarks.

Generating Fluent Adversarial Examples for Natural Languages (P19-1)

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Huangzhao Zhang, Hao Zhou, Ning Miao, Lei Li

Robustness in NLP

Challenge:	Current methods for building adversarial attackers for NLP are inefficient as the gradient is discarded.
Approach:	They propose an adversarial attacker which performs Metropolis-Hastings sampling with the guidance of gradients to solve these problems.
Outcome:	The proposed algorithm outperforms the baseline model on attacking capability on IMDB and SNLI.

Structure Guided Retrieval-Augmented Generation for Factual Queries (2026.acl-long)

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Miao Xie, Xiao Zhang, Yi Li, Chunli Lv

Information Retrieval

Challenge:	Existing methods for RAG produce factually incorrect outputs, resulting in incorrect answers.
Approach:	They propose a novel problem that explicitly incorporates structural information into RAG for factual questions to satisfy all query conditions.
Outcome:	The proposed method significantly outperforms baselines on ERQA while maintaining reasonable computational overhead.

MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)

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Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He

Green & Sustainable NLP

Challenge:	Document images are characterized by higher resolutions, denser content, and more complex structural layouts.
Approach:	They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition.
Outcome:	The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead.

Reinforced Product Metadata Selection for Helpfulness Assessment of Customer Reviews (D19-1)

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Miao Fan, Chao Feng, Mingming Sun, Ping Li

Representation Learning

Challenge:	a helpful review is largely concerned with the metadata of its target product . a selector learns from both the key-value product metadata and one of its reviews to take an action .
Approach:	They propose a framework that uses product metadata to assess helpfulness of free-text reviews . they use two real-world datasets from amazon.com and Yelp.com to test the framework .
Outcome:	The proposed framework can achieve state-of-the-art performance with substantial improvements . it uses two real-world datasets from Amazon.com and Yelp.com .

Safety is Not Only About Refusal: Reasoning-Enhanced Fine-tuning for Interpretable LLM Safety (2025.findings-acl)

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Yuyou Zhang, Miao Li, William Han, Yihang Yao, Zhepeng Cen, Ding Zhao

LLM-Based Human-Agent Collaboration and Interaction Systems: A Survey (2026.findings-acl)

Challenge:	Large Language Models (LLMs) are vulnerable to jailbreak attacks that exploit weaknesses in traditional safety alignment.
Approach:	They propose a framework that trains models to engage in explicit safe reasoning before response . they propose RATIONAL, which allows models to reject harmful prompts while providing meaningful and context-aware responses.
Outcome:	The proposed framework fine-tunes models to reason about query intent, ethics, and potential harm.

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Henry Peng Zou, Wei-Chieh Huang, Yaozu Wu, Jizhou Guo, Yankai Chen, Chunyu Miao, Hoang H Nguyen, Yue Zhou, Weizhi Zhang, Liancheng Fang, Hanrong Zhang, Fangxin Wang, Pengfei Zhang, Langzhou He, Yangning Li, Dongyuan Li, Renhe Jiang, Philip S. Yu

A Sentiment Consolidation Framework for Meta-Review Generation (2024.acl-long)

Challenge:	Recent advances in large language models (LLMs) have sparked growing interest in building fully autonomous agents.
Approach:	They propose to integrate human-provided information, feedback, or control into the agent system to enhance system performance, reliability, and safety.
Outcome:	The proposed systems improve system performance, reliability, and safety by integrating human-provided information, feedback, or control into the agent system.

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Miao Li, Jey Han Lau, Eduard Hovy

Low-Resource NLP

Challenge:	Recent advances in abstractive text summarization have created plausible summaries, but it is unclear if they truly possess the capability of information consolidation to generate summary.
Approach:	They propose to prompt large language models to generate meta-reviews and use evaluation metrics to assess the quality of generated meta- reviews.
Outcome:	The proposed framework proves that human meta-reviewers follow a framework of sentiment consolidation to write meta- reviews compared with prompting them with simple instructions.

A Topic Augmented Text Generation Model: Joint Learning of Semantics and Structural Features (D19-1)

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Hongyin Tang, Miao Li, Beihong Jin

Text Generation

Challenge:	Existing methods for text generation are limited in supervised setting and designed for specific applications.
Approach:	They propose a text generation model that learns semantics and structural features simultaneously . their model leverages a topic-based model to enhance the recognition of text semantics .
Outcome:	The proposed model outperforms state-of-the-art models in terms of text perplexity and topic coherence.

SRF: Enhancing Document-Level Relation Extraction with a Novel Secondary Reasoning Framework (2024.emnlp-main)

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Fu Zhang, Qi Miao, Jingwei Cheng, Hongsen Yu, Yi Yan, Xin Li, Yongxue Wu

Summarizing Multiple Documents with Conversational Structure for Meta-Review Generation (2023.findings-emnlp)

Challenge:	Existing methods for document-level relation extraction ignore bidirectional mention interaction when generating relational features for entity pairs.
Approach:	They propose a document-level relation extraction model that incorporates bidirectional mention fusion and a simple yet effective evidence extraction module for relation prediction.
Outcome:	The proposed model achieves SOTA performance and the proposed method is effective and general when integrated into existing models.

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Miao Li, Eduard Hovy, Jey Lau

Dialogue Systems & Conversational Agents

Challenge:	Existing models for abstractive text summarization do not provide explicit interdocument relationships among source documents.
Approach:	They propose a model that uses sparse attention based on the conversational structure and a multi-task training objective that predicts metadata features.
Outcome:	The proposed model outperforms baseline models in terms of evaluation metrics but struggle to handle conflicts in source documents.

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

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

Injecting Domain-Specific Knowledge into Large Language Models: A Comprehensive Survey (2025.findings-emnlp)

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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Zirui Song, Bin Yan, Yuhan Liu, Miao Fang, Mingzhe Li, Rui Yan, Xiuying Chen

ConNER: Consistency Training for Cross-lingual Named Entity Recognition (2022.emnlp-main)

Challenge:	specialized LLMs are often limited in domain-specific applications that require specialized knowledge.
Approach:	They provide a comprehensive overview of four key methods to enhance large language models by integrating domain-specific knowledge.
Outcome:	The proposed methods are categorized into four key approaches: dynamic knowledge injection, static knowledge embedding, modular adapters, and prompt optimization.

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Ran Zhou, Xin Li, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao

Multilinguality

Challenge:	Existing consistency training methods for named entity recognition (NER) are likely to violate the consistency hypothesis or focus on coarse-grain consistency.
Approach:	They propose a consistency training framework for cross-lingual named entity recognition that leverages unlabeled target-language data and dropout-based consistency training on labeled source-language datasets.
Outcome:	The proposed framework improves on translation-based consistency training on unlabeled target-language data and dropout-based consistent training on labeled source-language datasets.

Discovering Topics in Long-tailed Corpora with Causal Intervention (2021.findings-acl)

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Xiaobao Wu, Chunping Li, Yishu Miao

An Ensemble-of-Experts Framework for Rehearsal-free Continual Relation Extraction (2024.findings-acl)

Challenge:	Existing topic models are designed and evaluated on balanced corpora, but the longtailed bias can impair the performance of topic models.
Approach:	They propose a causal inference framework to explain topic modeling on long-tailed corpora by applying causal intervention in training.
Outcome:	The proposed model can mitigate the bias effect, greatly improve topic quality and discover the hidden semantics on the tail.

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Shen Zhou, Yongqi Li, Xin Miao, Tieyun Qian

Episodic Memory Retrieval from LLMs: A Neuromorphic Mechanism to Generate Commonsense Counterfactuals for Relation Extraction (2024.findings-acl)

Challenge:	Existing methods for continual relation extraction (CRE) are rehearsal-based and need to store samples and thus may encounter privacy and security issues.
Approach:	They propose an Ensemble-of-Experts framework for rehearsal-free continual relation extraction that discriminates between experts and augments analogous relations across tasks.
Outcome:	The proposed method outperforms existing rehearsal-free methods and is even better than existing methods.

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Xin Miao, Yongqi Li, Shen Zhou, Tieyun Qian

Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation (2021.eacl-main)

Challenge:	Large language models (LLMs) have achieved satisfactory performance in counterfactual generation, however, there are misalignments between LLMs and humans which hinder LLM from handling complex tasks like relation extraction.
Approach:	They propose to mimic the episodic memory retrieval mechanism of human hippocampus to align LLMs’ generation process with that of humans.
Outcome:	The proposed framework improves over existing methods in terms of quality of counterfactuals.

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Julia Ive, Andy Mingren Li, Yishu Miao, Ozan Caglayan, Pranava Madhyastha, Lucia Specia

AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)

Challenge:	Existing studies on multimodality in simultaneous machine translation have highlighted the challenges for the agent to maintain good translation quality while learning an optimal translation path.
Approach:	They propose a multimodal approach to simultaneous machine translation using reinforcement learning with strategies to integrate visual and textual information in both the agent and the environment.
Outcome:	The proposed multimodal approach improves translation quality while keeping latency low while providing visual cues.

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Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang

RelEdit: Evaluating Conceptual Knowledge Editing in Language Models via Relational Reasoning (2025.findings-acl)

Challenge:	Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning.
Approach:	AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks.
Outcome:	Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% .

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Yifan Niu, Miao Peng, Nuo Chen, Yatao Bian, Tingyang Xu, Jia Li

Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach (2022.emnlp-main)

Challenge:	Existing knowledge editing methods struggle to reason about related conceptual knowledge effectively, despite a lack of model-level relational reasoning.
Approach:	They propose a benchmark to assess concept-level and instance-level relational reasoning abilities of edited models.
Outcome:	The proposed model obtains the best scores on the memory-based in-context editing baseline, MICE, suggesting a promising direction for model editing.

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Miao Chen, Xinjiang Lu, Tong Xu, Yanyan Li, Zhou Jingbo, Dejing Dou, Hui Xiong

Text Generation

Challenge:	Currently, the generalization issues hinder the applicability of neural table-to-text models due to the limited source tables.
Approach:	They propose a table-structureaware text generation model with pretrained language model and propose TASD to bridge the gap between the structured table and text input.
Outcome:	The proposed model bridges the gap between the structured table and text input and generates accurate and fluent descriptive texts on two public datasets.

DeltaScore: Fine-Grained Story Evaluation with Perturbations (2023.findings-emnlp)

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Zhuohan Xie, Miao Li, Trevor Cohn, Jey Lau

Challenge:	Existing evaluation metrics for stories are limited in assessing intricate aspects of storytelling, such as fluency and interestingness.
Approach:	They propose a novel method that uses perturbation techniques to evaluate story aspects . they compare fluency, coherence, relatedness, logicality, interestingness and interestingness to existing metrics .
Outcome:	The proposed method shows that one specific perturbation is highly effective in capturing multiple aspects.

KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction (2024.acl-long)

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Zixuan Li, Yutao Zeng, Yuxin Zuo, Weicheng Ren, Wenxuan Liu, Miao Su, Yucan Guo, Yantao Liu, Lixiang Lixiang, Zhilei Hu, Long Bai, Wei Li, Yidan Liu, Pan Yang, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

ART: rule bAsed futuRe-inference deducTion (2023.emnlp-main)

Challenge:	None. None.. None!
Approach:	None. None.. None!
Outcome:	None. None. No. :

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Mengze Li, Tianqi Zhao, Bai Jionghao, Baoyi He, Jiaxu Miao, Wei Ji, Zheqi Lv, Zhou Zhao, Shengyu Zhang, Wenqiao Zhang, Fei Wu

Your Language Model May Think Too Rigidly: Achieving Reasoning Consistency with Symmetry-Enhanced Training (2025.findings-acl)

Challenge:	Existing studies focus on language-based premises and deduce valid conclusions from visual observations.
Approach:	They propose a rule-based deductive reasoning task that uses video to deduce the correct future event . they use commonsense knowledge to annotate video and a strong baseline to conduct reasoning .
Outcome:	Empirical studies validate the rationality of ARTNet in deductive reasoning upon visual observations . ART is a method that rigorously follows a set of explicit constraints to deduce valid conclusions from empirical facts .

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Yihang Yao, Zhepeng Cen, Miao Li, William Han, Yuyou Zhang, Emerson Liu, Zuxin Liu, Chuang Gan, Ding Zhao

MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER (2022.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated strong reasoning capabilities across various tasks.
Approach:	They propose a data-centric approach that enhances LLMs’ awareness of symmetry in query variations and propose syMmetry-ENhanceD (MEND) data augmentation.
Outcome:	Extensive experiments on logical and arithmetic reasoning tasks show that the proposed approach improves model robustness at the knowledge extraction stage through query augmentation.

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Ran Zhou, Xin Li, Ruidan He, Lidong Bing, Erik Cambria, Luo Si, Chunyan Miao

CARD: Cross-modal Agent Framework for Generative and Editable Residential Design (2025.emnlp-main)

Challenge:	Named entity recognition (NER) tasks have limited amount of labeled data . data augmentation methods suffer from token-label misalignment, which leads to unsatsifactory performance.
Approach:	They propose a data augmentation framework that explicitly injects NER labels into sentence context and generates high-quality augmented data with novel entities.
Outcome:	The proposed framework outperforms baseline methods on low-resource tasks.

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Pengyu Zeng, Jun Yin, Miao Zhang, Yuqin Dai, Jizhizi Li, ZhanXiang Jin, Shuai Lu

History-Aware Hierarchical Transformer for Multi-session Open-domain Dialogue System (2022.findings-emnlp)

Challenge:	Architectural design automation has made significant progress, but the complexity of open-world environments makes residential design a challenging task.
Approach:	They propose a framework that leverages a system of specialized cross-modal agents to adapt to open-world residential design.
Outcome:	The proposed framework enables users to generate and edit residential design without requiring specialized expertise.

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Tong Zhang, Yong Liu, Boyang Li, Zhiwei Zeng, Pengwei Wang, Yuan You, Chunyan Miao, Lizhen Cui

Dialogue Systems & Conversational Agents

Challenge:	Existing open-domain dialogue systems conduct one-session conversations, but multi-session MSCs are under-investigated.
Approach:	They propose a History-Aware Hierarchical Transformer for multi-session open-domain dialogue . they propose to encode history conversations into a history memory and leverage historical information to generate well-informed responses.
Outcome:	The proposed model outperforms baseline models on a large-scale MSC dataset.

Do you have the right scissors? Tailoring Pre-trained Language Models via Monte-Carlo Methods (2020.acl-main)

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Ning Miao, Yuxuan Song, Hao Zhou, Lei Li

How does Misinformation Affect Large Language Model Behaviors and Preferences? (2025.acl-long)

Challenge:	Pre-trained language models can be fine-tuned on task-specific datasets, but fine-timing can lead to over- and/or under-estimation problems.
Approach:	They propose a method to transfer probability mass from over-estimated regions to under-estimates by truncating and transferring probability mass between over- and under-estimating regions.
Outcome:	The proposed method outperforms the fine-tuning approach on a variety of datasets.

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Miao Peng, Nuo Chen, Jianheng Tang, Jia Li

Mitigating Spurious Correlations in Text Classification Using Latent Space Geometry (2026.acl-long)

Challenge:	Existing studies have explored the role of Large Language Models in combating misinformation, but there is still a lack of detailed analysis on the specific aspects and extent to which LLMs are influenced by misinformation.
Approach:	They propose to use a benchmark to evaluate LLMs' behavior and knowledge preference toward misinformation to identify their models.
Outcome:	The proposed approach is based on 10,346,712 pieces of misinformation and examines knowledge conflicts and stylistic variations.

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Jiasen Gao, Xiaoliang Chen, Duoqian Miao, Xu Gu, Xianyong Li, Yajun Du

Text Classification

Challenge:	Existing models rely on predictive shortcuts that hold in training data but break under distribution shifts, leading to large performance drops for minority groups.
Approach:	They propose a framework that transforms abstract biases into interpretable geometric anchors without auxiliary classifiers by manipulating latent space geometry.
Outcome:	The proposed framework outperforms state-of-the-art baselines and improves worst-group accuracy by over 20% on the CivilComments dataset.

Structural Reward Model: Enhancing Interpretability, Efficiency, and Scalability in Reward Modeling (2025.emnlp-industry)

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Xiaoyu Liu, Di Liang, Hongyu Shan, Peiyang Liu, Yonghao Liu, Muling Wu, Yuntao Li, Xianjie Wu, Li Miao, Jiangrong Shen, Minlong Peng

NeuSym-RAG: Hybrid Neural Symbolic Retrieval with Multiview Structuring for PDF Question Answering (2025.acl-long)

Challenge:	Generative RMs (GRMs) lack contextual and background information during inference, leading to incomplete evaluations.
Approach:	They propose a modular and interpretable framework that integrates side-branch models as auxiliary feature generators.
Outcome:	The proposed framework outperforms scalar and saline reward models in robustness and alignment with human preferences.

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Ruisheng Cao, Hanchong Zhang, Tiancheng Huang, Zhangyi Kang, Yuxin Zhang, Liangtai Sun, Hanqi Li, Yuxun Miao, Shuai Fan, Lu Chen, Kai Yu

Natural Language Interfaces

Challenge:	Existing approaches to retrieval augmented generation neglect PDF structure and layout . individual PDFs often exceed prompt limits and user queries may span multiple documents.
Approach:	They propose a hybrid neural symbolic retrieval framework which combines both paradigms in an interactive process.
Outcome:	The proposed framework organizes semi-structured PDF content into relational database and vectorstore . it defeats both RAG and structured baselines on three PDF-based QA datasets .

Prompting Large Language Models for Counterfactual Generation: An Empirical Study (2024.lrec-main)

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Yongqi Li, Mayi Xu, Xin Miao, Shen Zhou, Tieyun Qian

Pointing to a Llama and Call it a Camel: On the Sycophancy of Multimodal Large Language Models (2025.emnlp-main)

Challenge:	Large language models (LLMs) have made remarkable progress in a wide range of natural language understanding and generation tasks, but their ability to generate counterfactuals has not been examined systematically.
Approach:	They propose a framework to evaluate LLMs' ability to generate counterfactuals based on key factors including intrinsic properties and prompt design.
Outcome:	The proposed framework examines the strengths and weaknesses of large language models (LLMs) and identifies factors that influence their ability to generate counterfactuals.

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Renjie Pi, Kehao Miao, Li Peihang, Runtao Liu, Jiahui Gao, Jipeng Zhang, Xiaofang Zhou

Exploring Representation-level Augmentation for Code Search (2022.emnlp-main)

Challenge:	Multimodal large language models exhibit a pronounced form of visual sycophantic behavior when they process image inputs.
Approach:	They propose a technique that allows multimodal large language models to engage in reflective reasoning and determine whether a user’s instruction is misleading or corrective.
Outcome:	The proposed model resists misleading instructions but is stubborn even if it is wrong.

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Haochen Li, Chunyan Miao, Cyril Leung, Yanxian Huang, Yuan Huang, Hongyu Zhang, Yanlin Wang

Nested Event Extraction upon Pivot Element Recognition (2024.lrec-main)

Challenge:	Recent data augmentations for code search are at the raw-data level, which requires additional code analysis and training cost.
Approach:	They propose a general format of representation-level augmentation that unifies existing methods.
Outcome:	The proposed methods can boost the performance of code search models on a large-scale dataset.

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Weicheng Ren, Zixuan Li, Xiaolong Jin, Long Bai, Miao Su, Yantao Liu, Saiping Guan, Jiafeng Guo, Xueqi Cheng

pFedGPT: Hierarchically Optimizing LoRA Aggregation Weights for Personalized Federated GPT Models (2025.emnlp-main)

Challenge:	Nested Event Extraction (NEE) aims to extract complex event structures where an event contains other events as its arguments recursively.
Approach:	They propose a new model that extracts nested events mainly based on recognizing PEs.
Outcome:	The proposed model can extract nested events based on recognizing PEs . it incorporates information from both event types and argument roles to improve performance .

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Zhanming Shen, Tianqi Xu, Hao Wang, Jian Li, Miao Pan

Improving Self-training for Cross-lingual Named Entity Recognition with Contrastive and Prototype Learning (2023.acl-long)

Challenge:	Existing methods for fine-tuning Large Language Models (LLMs) struggle with data heterogeneity and adapt shared global knowledge to individual client needs.
Approach:	They propose a framework that leverages Hierarchical Bayesian Optimization (HBO) for fine-grained, personalized LoRA aggregation.
Outcome:	The proposed framework achieves state-of-the-art (SOTA) performance on personalized FL benchmarks while introducing only minimal (approx. 4%) additional optimization overhead.

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Ran Zhou, Xin Li, Lidong Bing, Erik Cambria, Chunyan Miao

Visual Contextual Attack: Jailbreaking MLLMs with Image-Driven Context Injection (2025.emnlp-main)

Challenge:	Existing methods to bridge the linguistic gap between self-training and monolingual named entity recognition (NER) however, due to sub-optimal performance on target languages, the pseudo labels are noisy and limit the overall performance.
Approach:	They propose to combine representation learning and pseudo label refinement in one coherent framework to improve self-training for cross-lingual named entity recognition (NER)
Outcome:	The proposed method improves cross-lingual named entity recognition (NER) on multiple transfer pairs.

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Miao Ziqi, Yi Ding, Lijun Li, Jing Shao

Firm or Fickle? Evaluating Large Language Models Consistency in Sequential Interactions (2025.findings-acl)

Challenge:	Recent studies have shown that visual encoders can induce harmful behavior in multimodal large language models.
Approach:	They propose a vision-centric jailbreak attack that uses visual information to create a jailbreak context.
Outcome:	The proposed attack outperforms baseline attacks on MM-SafetyBench and GPT-4o.

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Yubo Li, Yidi Miao, Xueying Ding, Ramayya Krishnan, Rema Padman

Exploring All-In-One Knowledge Distillation Framework for Neural Machine Translation (2023.emnlp-main)

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, but their deployment in high-stake domains requires consistent and coherent behavior across multiple rounds of user interaction.
Approach:	They propose a framework for evaluating and improving LLM response consistency, and introduce a benchmark dataset to evaluate LLM consistency.
Outcome:	The proposed framework improves response stability without sacrificing accuracy, and offers a practical path toward more dependable behavior in critical, real-world deployments.

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Zhongjian Miao, Wen Zhang, Jinsong Su, Xiang Li, Jian Luan, Yidong Chen, Bin Wang, Min Zhang

On the Step Length Confounding in LLM Reasoning Data Selection (2026.findings-acl)

Challenge:	Existing knowledge distillation methods only obtain one lightweight student each time . this could be resource-intensive and resulting in multiple students not being optimally utilized .
Approach:	They propose a knowledge distillation framework which generates multiple satisfactory students at once.
Outcome:	The proposed framework generates multiple satisfactory students at once.

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Bing Wang, Rui Miao, Chen Shen, Shaotian Yan, Kaiyuan Liu, Ximing Li, Xiaosong Yuan, Sinan Fan, Jun Zhang, Jieping Ye

SAM3-I: Segment Anything with Instructions (2026.acl-long)

Challenge:	Existing pipelines generate long reasoning data from more capable Large Language Models (LLMs) and apply manually heuristic or naturalness-based selection methods to filter high-quality samples.
Approach:	They propose to use supervised fine-tuning to generate long reasoning data from more capable Large Language Models and apply manually heuristic or naturalness-based selection methods to filter high-quality samples.
Outcome:	Experiments on four LLMs and five evaluation benchmarks show that the proposed approach is effective in mitigating step length confounding problem.

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Jingjing Li, Yue Feng, Yuchen Guo, Jincai Huang, Wei Ji, Qi Bi, Yongri Piao, Miao Zhang, Xiaoqi Zhao, Qiang Chen, Shihao Zou, Huchuan Lu, Li Cheng

Low-Resource NLP

Challenge:	Existing methods for concept-level grounding and instruction-level reasoning use coarse representations and iterative mask filtering.
Approach:	They propose an instruction-following extension of the Segment Anything Model 3 family that unifies concept-level grounding and instruction-level reasoning within a single segmentation framework.
Outcome:	Experiments show that SAM3-I achieves appealing performance across referring and reasoning-based segmentation while maintaining its strong concept recall ability.

A Secure and Efficient Federated Learning Framework for NLP (2021.emnlp-main)

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Chenghong Wang, Jieren Deng, Xianrui Meng, Yijue Wang, Ji Li, Sheng Lin, Shuo Han, Fei Miao, Sanguthevar Rajasekaran, Caiwen Ding

Green & Sustainable NLP

Challenge:	Existing FL frameworks require a trusted aggregator or require heavy-weight cryptographic primitives, which makes the performance significantly degraded.
Approach:	They propose a framework that is federated and efficient for NLP . they propose to eliminate the need for trusted entities and achieve better model accuracy .
Outcome:	The proposed framework achieves better model accuracy and model accuracy than existing FL frameworks.

Multilingual Retrieval Augmented Generation for Culturally-Sensitive Tasks: A Benchmark for Cross-lingual Robustness (2025.findings-acl)

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Bryan Li, Fiona Luo, Samar Haider, Adwait Agashe, Siyu Li, Runqi Liu, Miranda Muqing Miao, Shriya Ramakrishnan, Yuan Yuan, Chris Callison-Burch

Multilinguality

Challenge:	Existing approaches to retrieval-augmented generated (RAG) can be useful in multilingual settings, but they also introduce biases in the retrieved documents.
Approach:	They propose a dataset of territorial disputes paired with retrieved Wikipedia documents in 49 languages to evaluate cross-lingual robustness.
Outcome:	The proposed paradigm helps mitigate hallucinations of large language models (LLMs).

Generating Commonsense Counterfactuals for Stable Relation Extraction (2023.emnlp-main)

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Xin Miao, Yongqi Li, Tieyun Qian

Beyond Dialogue Time: Temporal Semantic Memory for Personalized LLM Agents (2026.findings-acl)

Challenge:	Existing methods for relation extraction struggle to identify causal terms under the invariant entity constraint.
Approach:	They propose a framework to generate commonsense counterfactuals for stable relation extraction by using a knowledge base WordNet and a constituency parser.
Outcome:	The proposed framework significantly enhances the stability of relation extraction models.

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Miao Su, Yucan Guo, Zhongni Hou, Long Bai, Zixuan Li, Yufei Zhang, Guojun Yin, Wei Lin, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

Dialogue Systems & Conversational Agents

Challenge:	Existing methods focus on point-wise memory, losing durative information that captures persistent states and evolving patterns.
Approach:	They propose a memory framework that models semantic time for point-wise memory and supports the construction and utilization of durative memory.
Outcome:	Experiments on LongMemEval and LoCoMo show that the proposed method outperforms existing methods and achieves up to 12.2% improvement in accuracy.

STANKER: Stacking Network based on Level-grained Attention-masked BERT for Rumor Detection on Social Media (2021.emnlp-main)

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Dongning Rao, Xin Miao, Zhihua Jiang, Ran Li

The Impact of Language Mixing on Bilingual LLM Reasoning (2025.emnlp-main)

Challenge:	Existing models for text classification are limited in performance, resulting in poor rumor detection.
Approach:	They propose to use Chinese microblogs to detect rumors using pre-trained language models and auxiliary features such as comments to mask co-attention.
Outcome:	The proposed model outperforms the state-of-the-art on Weibo20 and three existing social media datasets.

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Yihao Li, Jiayi Xin, Miranda Muqing Miao, Qi Long, Lyle Ungar

Few-Shot Semantic Dependency Parsing via Graph Contrastive Learning (2024.lrec-main)

Challenge:	Recent studies show multilingual speakers intentionally switch languages during reasoning . enforcing monolingual decoding reduces accuracy by 5.6 percentage points .
Approach:	They find that multilingual speakers intentionally switch languages during reasoning . enforcing monolingual decoding reduces accuracy by 5.6 percentage points . authors suggest that language mixing is not merely a byproduct of multilingual training .
Outcome:	The proposed model can be used to predict whether a language switch would benefit or harm reasoning.

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Bin Li, Yunlong Fan, Yikemaiti Sataer, Chuanqi Shi, Miao Gao, Zhiqiang Gao

MECH: A Cost-Effective Multi-Task Cascade Framework for Classroom Opinion Evolution Recognition (2026.acl-long)

Challenge:	Existing graph neural networks (GNNs) have shown promising performance on semantic dependency parsing (SDP) training a high-performing model requires a large amount of labeled data and it is prone to over-fitting in the absence of sufficient labele .
Approach:	They propose a syntax-guided graph contrastive learning framework to train GNNs with unlabeled data and fine-tune pre-trained GNN models with few-shot labeled SDP data.
Outcome:	The proposed framework achieves promising results when few-shot training samples are available.

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Yancui Li, Xiaoyu Zhou, Guoyi Miao, Fang Kong

Decomposed Opinion Summarization with Verified Aspect-Aware Modules (2025.findings-acl)

Challenge:	Existing studies focus on dialogue act annotation, overlooking the deeper dimension of opinion evolution.
Approach:	They propose a framework for Classroom Opinion Evolution Recognition that translates "Action-Opinion" dualism into a risk-aware routing mechanism.
Outcome:	The proposed framework achieves state-of-the-art accuracy of 78.55% while reducing API costs by 44.4%.

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Miao Li, Jey Han Lau, Eduard Hovy, Mirella Lapata

Summarization

Challenge:	Existing methods for summarizing opinions from large-scale online reviews are not available for crowdsourcing and are difficult to crowdsource.
Approach:	They propose a domain-agnostic modular approach guided by review aspects to separate tasks of aspect identification, opinion consolidation, and meta-review synthesis to enable greater transparency and ease of inspection.
Outcome:	The proposed approach generates more grounded summaries than baseline models, as verified through automated and human evaluations.

Easy Dataset: A Unified and Extensible Framework for Synthesizing LLM Fine-Tuning Data from Unstructured Documents (2025.emnlp-demos)

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Ziyang Miao, Qiyu Sun, Jingyuan Wang, Yuchen Gong, Yaowei Zheng, Shiqi Li, Richong Zhang

RASPberry: Retrieval-Augmented Monte Carlo Tree Self-Play with Reasoning Consistency for Multi-Hop Question Answering (2025.findings-acl)

Challenge:	Existing data synthesis tools struggle to extract reliable fine-tuning data from heterogeneous documents.
Approach:	They propose a framework for synthesizing fine-tuning data from unstructured documents via an intuitive graphical user interface.
Outcome:	The proposed framework can extract reliable data from unstructured documents via an intuitive graphical user interface (GUI) it leverages persona-driven prompting approach to generate diverse question-answer pairs using public-available LLMs.

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Baixuan Li, Yunlong Fan, Tianyi Ma, Miao Gao, Chuanqi Shi, Zhiqiang Gao

Short Text Topic Modeling with Topic Distribution Quantization and Negative Sampling Decoder (2020.emnlp-main)

Challenge:	Existing methods for generating and analyzing multiple document knowledge are not effective for multi-hop question answering.
Approach:	They propose a Monte Carlo tree-based approach to inference-time scaling using RASPberry.
Outcome:	Experimental results show that the proposed method achieves better inference-time scaling on smaller LLMs.

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Xiaobao Wu, Chunping Li, Yan Zhu, Yishu Miao

Breaking the Hourglass Phenomenon of Residual Quantization: Enhancing the Upper Bound of Generative Retrieval (2024.emnlp-industry)

Challenge:	Topic models for short texts suffer from data sparsity because of limited word co-occurrences.
Approach:	They propose a neural topic model with a new topic distribution quantization approach that generates peakier distributions that are more appropriate for modeling short texts.
Outcome:	The proposed model outperforms both strong traditional and neural baselines under extreme data sparsity scenes, producing high-quality topics.

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Zhirui Kuai, Zuxu Chen, Huimu Wang, Mingming Li, Dadong Miao, Wang Binbin, Xusong Chen, Li Kuang, Yuxing Han, Jiaxing Wang, Guoyu Tang, Lin Liu, Songlin Wang, Jingwei Zhuo

Information Retrieval

Challenge:	Generative retrieval (GR) is a transformative paradigm in search and recommender systems . however, data sparsity and long-tailed distribution hinder the full utilization of GR .
Approach:	They propose a method to reduce the "Hourglass" phenomenon in RQ-SID where codebook tokens become overly concentrated.
Outcome:	The proposed methods improve retrieval efficiency and generalization capabilities.

NewsBench: A Systematic Evaluation Framework for Assessing Editorial Capabilities of Large Language Models in Chinese Journalism (2024.acl-long)

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Miao Li, Ming-Bin Chen, Bo Tang, ShengbinHou ShengbinHou, Pengyu Wang, Haiying Deng, Zhiyu Li, Feiyu Xiong, Keming Mao, Cheng Peng, Yi Luo

DynGL-SDP: Dynamic Graph Learning for Semantic Dependency Parsing (2022.coling-1)

Challenge:	a novel evaluation framework assesses the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism.
Approach:	They propose to use a benchmark dataset to assess the capabilities of Large Language Models (LLMs) for editorial capabilities in Chinese journalism.
Outcome:	The proposed evaluation framework is based on a dataset of 1,267 test samples in 24 news domains.

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Bin Li, Miao Gao, Yunlong Fan, Yikemaiti Sataer, Zhiqiang Gao, Yaocheng Gui

Structured Data in NLP

Challenge:	Existing parsers that learn graph representations based on static graphs are error-prone and disjointed . Graph-based parser can parse sentences efficiently but suffer from error propagation .
Approach:	They propose a dynamic graph learning framework to learn graph representations based on a static graph constructed by an existing parser.
Outcome:	The proposed parser outperforms the previous parsers on the SemEval-2015 task 18 dataset in three languages.

Multi-modal Action Chain Abductive Reasoning (2023.acl-long)

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Mengze Li, Tianbao Wang, Jiahe Xu, Kairong Han, Shengyu Zhang, Zhou Zhao, Jiaxu Miao, Wenqiao Zhang, Shiliang Pu, Fei Wu

The GaoYao Benchmark: A Comprehensive Framework for Evaluating Multilingual and Multicultural Abilities of Large Language Models (2026.acl-long)

Challenge:	Existing models for Abductive Reasoning are limited in their ability to infer the most plausible explanation of incomplete known phenomena.
Approach:	They propose a vision-language task that aims to imagine the most plausible event by spatio-temporal grounding in past video and infer the hypothesis of subsequent action chain layer by layer.
Outcome:	The proposed model outperforms existing video-language models in terms of effectiveness on the proposed dataset.

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Yilun Liu, Chunguang Zhao, Mengyao Piao, Lingqi Miao, Shimin Tao, Minggui HE, Chenxin Liu, Zhang Li, null Mahongxia, Jiaxin Guo, Chen Liu, Liqun Deng, Jiansheng Wei, Xiaojun Meng, Fanyi Du, Daimeng Wei, Yanghua Xiao

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

Challenge:	Existing multilingual evaluation benchmarks neglect cultural nuances and lack language coverage in subjective tasks.
Approach:	They propose a framework that categorizes evaluation tasks into three cultural layers and nine cognitive sub-layers.
Outcome:	The proposed framework surpasses prior coverage by up to 111% on 20+ LLMs.

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

Beyond Surface Features: Advancing Medical Vision-Language Alignment via Dynamic Evidence-Guided Preference Optimization (2026.acl-long)

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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Zixuan Huang, Zhihong Zhu, Xiaolong Liu, Yanchao Hao, Manman Zhang, Zheng Wei, Bowen Xing, Xian Wu, Ye Li, Fen Miao, Yefeng Zheng

A Survey of Link Prediction in N-ary Knowledge Graphs (2025.emnlp-main)

Challenge:	Existing preference-based methods for medical large vision-Language Models face limitations in medical settings . existing methods are limited by overfitting to superficial cues and pseudo convergence of the preference signal.
Approach:	They propose a framework that enables evidence-aware and adaptive preference learning for Med-LVLMs.
Outcome:	The proposed framework improves evidence-aware and adaptive preference learning for Med-LVLMs.

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Jiyao Wei, Saiping Guan, Da Li, Zhongni Hou, Miao Su, Yucan Guo, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng

Knowledge Representation

Challenge:	N-ary Knowledge Graphs (NKGs) capture n-ary facts containing more than two entities.
Approach:	They present the first comprehensive survey of link prediction in NKGs . they provide an overview of the field and analyze their performance and application scenarios .
Outcome:	The proposed methods provide an overview of the field and analyze performance and application scenarios.

Rethinking Negative Pairs in Code Search (2023.emnlp-main)

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Haochen Li, Xin Zhou, Anh Luu, Chunyan Miao

Generative Dense Retrieval: Memory Can Be a Burden (2024.eacl-long)

Challenge:	Comparative learning is a key component in fine-tuning code search models . however, negative samples of InfoNCE may deteriorate its representation learning .
Approach:	They propose a loss function that inserts weight terms into InfoNCE to improve contrastive learning.
Outcome:	The proposed loss function is a special case of Soft-InfoNCE, the authors show . it is more accurate than other loss functions, and it is faster than other models.

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Peiwen Yuan, Xinglin Wang, Shaoxiong Feng, Boyuan Pan, Yiwei Li, Heda Wang, Xupeng Miao, Kan Li

Text Clustering

Challenge:	Empirical results show that Generative Dense Retrieval (GDR) achieves an average of 3.0 R@100 improvement on NQ dataset under multiple settings and has better scalability.
Approach:	They propose a Generative Dense Retrieval paradigm that auto-decodes document identifiers given a query and uses memory to avoid memory confusion.
Outcome:	Empirical results show that the proposed paradigm improves on the small-scale corpora and improves scalability.

Deep Research with Open-Domain Evaluation and Multi-Stage Guardrails for Safety (2026.acl-long)

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Wei-Chieh Huang, Henry Peng Zou, Yaozu Wu, Dongyuan Li, Yankai Chen, Weizhi Zhang, Yangning Li, Angelo Zangari, Jizhou Guo, Chunyu Miao, Liancheng Fang, Langzhou He, Yinghui Li, Renhe Jiang, Philip S. Yu

TF-Mamba: Text-enhanced Fusion Mamba with Missing Modalities for Robust Multimodal Sentiment Analysis (2025.findings-emnlp)

Challenge:	Existing deep research frameworks lack adequate evaluation procedures and stage-specific protections.
Approach:	They propose a framework with open-domain evaluation and a stage-wise safety benchmark to address this oversight.
Outcome:	The proposed framework improves defense success rates by 16.53% while reducing over-refusal rates to approximately 6%.

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Xiang Li, Xianfu Cheng, Dezhuang Miao, Xiaoming Zhang, Zhoujun Li

Long-Context Reasoning Through Proxy-Based Chain-of-Thought Tuning (2026.acl-long)

Challenge:	Existing Transformer-based methods with missing modalities are difficult to use and have quadratic complexity.
Approach:	They propose a text-enhanced Fusion Mamba framework for robust MSA with missing modalities . a Text-aware Modality Enhancement module aligns and enriches non-text modality while reconstructing missing text semantics.
Outcome:	The proposed method is efficient under missing modalities and can be used in long-range modeling and multimodal fusion scenarios.

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Miao Li, Irina Saparina, Alexander Gurung, Mirella Lapata

CodecLM: Aligning Language Models with Tailored Synthetic Data (2024.findings-naacl)

Challenge:	Recent large language models support inputs of up to 10 million tokens, yet they perform poorly on long-context tasks.
Approach:	They propose a training framework that transfers reasoning capabilities from proxy contexts to full long contexts.
Outcome:	The proposed framework outperforms baseline models with reduced computational overhead.

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Zifeng Wang, Chun-Liang Li, Vincent Perot, Long Le, Jin Miao, Zizhao Zhang, Chen-Yu Lee, Tomas Pfister

Unlocking the Black Box of Latent Reasoning: An Interpretability-Guided Approach to Intervention (2026.acl-long)

Challenge:	Recent work on generating diverse instructions and applying LLM to increase instruction complexity neglects downstream use cases.
Approach:	They propose a framework for generating high-quality synthetic data for LLM alignment with different downstream instruction distributions and LLMs.
Outcome:	Experiments on four open-domain instruction using the proposed framework validate the effectiveness of CodecLM over the current state-of-the-art.

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Shuochen Chang, Tong Bai, Xiaofeng Zhang, Qianli Ma, Qingyang Liu, Zhaohe Liao, Yibo Miao, Li Niu