Papers by Chen Xinyu

TC–RAG: Turing–Complete RAG’s Case study on Medical LLM Systems (2025.acl-long)

Challenge:	Recent studies show that Large Language Models (LLMs) have shown remarkable intelligence in question answering.
Approach:	They propose to reframe the Question Answering task as Programming to overcome this limitation by leveraging LLMs' superior ability in understanding both natural language and programming language.
Outcome:	The proposed approach improves on time-sensitive question answering datasets by 14.5% over baselines.

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Xinke Jiang, Yue Fang, Rihong Qiu, Haoyu Zhang, Yongxin Xu, Hao Chen, Wentao Zhang, Ruizhe Zhang, Yuchen Fang, Xinyu Ma, Xu Chu, Junfeng Zhao, Yasha Wang

Information Retrieval

Challenge:	Existing approaches to RAG neglect system state variables, resulting in poor performance and erroneous knowledge accumulation.
Approach:	They propose a framework that incorporates a Turing Complete System to manage state variables and manage retrieval halting.
Outcome:	The proposed framework improves on seven real-world healthcare datasets and shows that it is more accurate than existing methods.

Reinforced Efficient Reasoning via Semantically Diverse Exploration (2026.acl-long)

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Ziqi Zhao, Zhaochun Ren, Jiahong Zou, Liu Yang, Zhiwei Xu, Xuri Ge, Zhumin Chen, Xinyu Ma, Daiting Shi, Shuaiqiang Wang, Dawei Yin, Xin Xin

Aligning Large Language Models via Fully Self-Synthetic Data (2026.acl-long)

Challenge:	Existing methods for reinforcement learning with verifiable rewards suffer from limited exploration diversity and inefficient reasoning.
Approach:	They propose a method that rewards concise and correct reasoning while penalizing unnecessarily long reasoning chains.
Outcome:	Extensive experiments on Qwen and Llama models validate the effectiveness and efficiency of ROSE.

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Shangjian Yin, Zhepei Wei, Xinyu Zhu, Wei-Lin Chen, Yu Meng

Cognitive Visual-Language Mapper: Advancing Multimodal Comprehension with Enhanced Visual Knowledge Alignment (2024.acl-long)

Challenge:	Existing approaches to reinforcement learning from human feedback (RLHF) require expensive human-annotated datasets and proprietary models like GPT-4 to annotate preference pairs.
Approach:	They propose a self-synthetic framework for LLM alignment where all training data, including prompts (i.e., user queries), responses, and preferences, are generated by the model itself.
Outcome:	The proposed framework enhances the model’s chat capabilities on standard benchmarks like AlpacaEval 2.0 while maintaining strong performance on downstream objective tasks.

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Yunxin Li, Xinyu Chen, Baotian Hu, Haoyuan Shi, Min Zhang

Emergent Relational Order in LLM Agent Societies: From Collective Affect to Authority Stratification (2026.findings-acl)

Challenge:	Recent Large Multimodal Models (LMMs) focus on visual knowledge-dimension alignment, but ignore visual knowledge.
Approach:	They propose a cognitive visual-language mapper that integrates visual-linguistic knowledge alignment with a fine-grained knowledge Adapter.
Outcome:	The proposed model significantly improves LMMs on knowledge-based visual question answering (VQA) it also improves the performance of other models, including GPT-4V and Gemini-Pro.

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Zhiyuan Ji, Xinyu Chen, Ziqi Dai, Shiyun Tang, Chunyu Wei, Yueguo Chen

Linguistic Theories

Challenge:	Fei Xiaotong’s Differential Order Pattern characterizes rural society as egocentric and relationally graded, with cooperation attenuating over social distance.
Approach:	They propose a multi-agent framework grounded in Affect Control Theory, Social Identity Theory, and Durkheimian collective affect.
Outcome:	Extensive simulations support interpreting Differential Order as a structure-sensitive emergent outcome of general social mechanisms.

Combining Character and Word Information in Neural Machine Translation Using a Multi-Level Attention (N18-1)

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Huadong Chen, Shujian Huang, David Chiang, Xinyu Dai, Jiajun Chen

Machine Translation

Challenge:	Neural machine translation models learn to map from source language sentences to target language sentences via continuous-space intermediate representations.
Approach:	They propose an encoder with character attention which augments the (sub)word-level representation with character-level information and a decoder with multiple attentions that enable the representations from different levels of granularity to control the translation cooperatively.
Outcome:	The proposed model outperforms the standard word-based model, subword-based models, and strong character-based ones on translation tasks.

TriEx: A Game-based Tri-View Framework for Explaining Internal Reasoning in Multi-Agent LLMs (2026.acl-long)

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Ziyi Wang, Chen Zhang, Wenjun Peng, Qi Wu, Xinyu Wang

Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models (2024.findings-acl)

Challenge:	Existing explainability methods for large language models have been limited in capturing interaction-dependent belief dynamics and multi-agent reasoning.
Approach:	They propose a tri-view explainability framework that instruments sequential decision making with aligned artifacts.
Outcome:	The proposed framework enables analysis of explanation faithfulness, belief dynamics, and evaluator reliability, revealing systematic mismatches between what agents say, what they believe, and what they do.

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Mingda Li, Xinyu Li, Yifan Chen, Wenfeng Xuan, Weinan Zhang

Beyond Surface-Level Pattern Trap: LLM Agents for Faster and Smarter Cross-Architecture Code Migration (2026.findings-acl)

Challenge:	Retrieval-Augmented Large Language Models (RALMs) do not consistently outperform the original retrieval-free Language Model (LM).
Approach:	They propose a trainable framework that can adaptively retrieve from different knowledge sources and effectively decrease unpredictable reader errors.
Outcome:	The proposed framework significantly improves performance over the RALM with a single retriever by significantly reducing inconsistent behaviors.

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Weijia Li, KE Gao, Pengfei Chen, Jiajie Li, Xinyu Wang, Yiran Le, Yize Wu, Ling Li

Reasoning

Challenge:	cross-architecture code migration is a resource-intensive and errorprone task.
Approach:	a framework for cross-architecture code migration is proposed to decouple implementation details through functional mining and code refactoring.
Outcome:	a new framework improves performance and correctness over state-of-the-art frameworks on OpenCV migration tasks.

ToolBeHonest: A Multi-level Hallucination Diagnostic Benchmark for Tool-Augmented Large Language Models (2024.emnlp-main)

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Yuxiang Zhang, Jing Chen, Junjie Wang, Yaxin Liu, Cheng Yang, Chufan Shi, Xinyu Zhu, Zihao Lin, Hanwen Wan, Yujiu Yang, Tetsuya Sakai, Tian Feng, Hayato Yamana

Reasoning

Challenge:	Currently, tool-augmented large language models (LLMs) only achieve total scores of 45.3 and 37.0, respectively, on a scale of 100.
Approach:	They propose a multi-level diagnostic process to assess the LLM's hallucinations through two perspectives: depth and breadth.
Outcome:	The proposed diagnostic process assesses the hallucinations of large language models through two perspectives: depth and breadth.

Bit-Flip Error Resilience in LLMs: A Comprehensive Analysis and Defense Framework (2025.emnlp-main)

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Yuhang Chen, Zhen Tan, Ajay Kumar Jaiswal, Huaizhi Qu, Xinyu Zhao, Qi Lin, Yu Cheng, Andrew Kwong, Zhichao Cao, Tianlong Chen

Debiasing the Fine-Grained Classification Task in LLMs with Bias-Aware PEFT (2025.acl-long)

Challenge:	Bit-flip errors (BFEs) are hardware faults where individual bits in memory or processing units are unintentionally flipped.
Approach:	They propose a novel defense strategy to mitigate bit-flip errors (BFEs) they propose bfe protection and a self-correction mechanism to minimize performance degradation .
Outcome:	The proposed defense strategy minimizes performance degradation while significantly improving robustness against BFEs.

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Daiying Zhao, Xinyu Yang, Hang Chen

UQ-Merge: Uncertainty Guided Multimodal Large Language Model Merging (2025.findings-acl)

Challenge:	Existing methods to mitigate label biases such as retraining, post-hoc adjustment, and parameter-efficient fine-tuning fail to address prediction propensity and discriminative ability biase.
Approach:	They propose a bias-aware optimization framework that incorporates two distinct label balance constraints with a PEFT strategy targeting an intermediate layer to mitigate this issue.
Outcome:	The proposed approach outperforms or matches the performance of full-parameter fine-tuning and LoRA, achieving superior results with lower perplexity.

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Huaizhi Qu, Xinyu Zhao, Jie Peng, Kwonjoon Lee, Behzad Dariush, Tianlong Chen

NOVER: Incentive Training for Language Models via Verifier-Free Reinforcement Learning (2025.emnlp-main)

Challenge:	Existing models merging methods often lead to suboptimal performance due to harmful models . et al., 2018; 59: 59-64.
Approach:	They propose an uncertainty-guided MLLM merging algorithm that integrates models into a single MLML.
Outcome:	The proposed algorithm improves on held-in and held-out vision-language benchmarks.

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Wei Liu, Siya Qi, Xinyu Wang, Chen Qian, Yali Du, Yulan He

Explicit Semantic Decomposition for Definition Generation (2020.acl-main)

Challenge:	Recent advances in reinforcement learning, such as DeepSeek R1-Zero, highlight the effectiveness of incentive training, but these methods rely on external verifiers, which limits their applicability to domains like mathematics and coding, where such verifier is readily available.
Approach:	They propose a general reinforcement learning framework that requires only standard supervised fine-tuning data with no need for an external verifier.
Outcome:	The proposed framework outperforms the model of the same size distilled from large reasoning models such as DeepSeek R1 671B by 7.7%.

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Jiahuan Li, Yu Bao, Shujian Huang, Xinyu Dai, Jiajun Chen

Challenge:	Existing definition generation methods rely on decoding to extract semantic components of words.
Approach:	They propose a method which explicitly decomposes meaning of words into semantic components and models them with discrete latent variables for definition generation.
Outcome:	The proposed method outperforms existing methods on WordNet and Oxford benchmarks.

Are LLM-based Evaluators Confusing NLG Quality Criteria? (2024.acl-long)

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Xinyu Hu, Mingqi Gao, Sen Hu, Yang Zhang, Yicheng Chen, Teng Xu, Xiaojun Wan

Text Classification

Challenge:	Existing studies show that LLMs confuse evaluation criteria, which reduces their reliability.
Approach:	They propose a hierarchical classification system for 11 common aspects with corresponding different evaluation criteria.
Outcome:	The proposed system is based on 11 common aspects with different evaluation criteria.

KnowDis: Knowledge Enhanced Data Augmentation for Event Causality Detection via Distant Supervision (2020.coling-main)

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Xinyu Zuo, Yubo Chen, Kang Liu, Jun Zhao

Knowledge Representation

Challenge:	Existing methods of event causality detection use hand-labeled training data.
Approach:	They propose a framework for event causality detection that augments training data via distant supervision.
Outcome:	The proposed framework outperforms existing methods on two benchmark datasets . it outperformed previous methods by a large margin assisted with automatically labeled training data.

RoBSA: RoPE-based Blockwise Sparse Multi-head Latent Attention (2026.acl-long)

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Xinyu Shi, Kairong Luo, Zhen Zheng, Wenguang Chen

Reasoning Like Program Executors (2022.emnlp-main)

Challenge:	Large Language Models (LLMs) have advanced in recent years, scaling up in both parameter count and context length.
Approach:	They propose a method to compute attention over a subset of context tokens and to implement token selection in a blockwise manner.
Outcome:	The proposed method reduces end-to-end inference latency by up to 2.55x with minimal accuracy loss compared to full attention in long-context scenarios for very large models.

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Xinyu Pi, Qian Liu, Bei Chen, Morteza Ziyadi, Zeqi Lin, Qiang Fu, Yan Gao, Jian-Guang Lou, Weizhu Chen

Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification (2022.coling-1)

Challenge:	Existing language models are inadequate in reasoning, according to studies . a new reasoning pre-training paradigm is based on pretraining language models with programs .
Approach:	They propose a reasoning pre-training paradigm that empowers language models to harvest reasoning knowledge possessed by program executors.
Outcome:	The proposed reasoning pre-training paradigm can boost models' reasoning skills . it can be instantiated by different kinds of program executors and run on a single database .

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Fei Zhao, Zhen Wu, Siyu Long, Xinyu Dai, Shujian Huang, Jiajun Chen

Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction (2026.findings-acl)

Challenge:	Existing methods to determine sentiment polarity of opinion target are inconsistent and lack visual attention.
Approach:	They propose a framework which can exploit adjective-noun pairs extracted from images to improve visual attention and sentiment prediction capability of the TMSC task.
Outcome:	The proposed framework outperforms state-of-the-art on two public datasets.

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Xu Shen, Qi Zhang, Song Wang, Zhen Tan, Xinyu Zhao, Laura Yao, Vaishnav Tadiparthi, Hossein Nourkhiz Mahjoub, Ehsan Moradi Pari, Kwonjoon Lee, Tianlong Chen

SCoder: Progressive Self-Distillation for Bootstrapping Small-Scale Data Synthesizers to Empower Code LLMs (2025.findings-emnlp)

Challenge:	Large Language Model based multi-agent systems (MAS) excel at collaborative problem solving but remain brittle to cascading errors.
Approach:	They propose a metacognitive framework that enables step-level error detection and self-correction in Large Language Model based multi-agent systems (MAS) .
Outcome:	The proposed framework outperforms baselines on the Who When benchmark and delivers consistent gains on AgentErrorBench.

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Xinyu Zhang, Changzhi Zhou, Linmei Hu, Luhao Zhang, Xiancai Chen, Haomin Fu, Yang Yang, Mengdi Zhang

Alleviating the Inequality of Attention Heads for Neural Machine Translation (2022.coling-1)

Challenge:	Existing code large language models rely on large-scale instruction data distilled from proprietary LLMs for fine-tuning, which typically incurs high costs.
Approach:	They propose an iterative self-distillation approach to bootstrap small-scale LLMs . they use large-scale instruction data distilled from proprietary LLM for fine-tuning .
Outcome:	The proposed method reduces reliance on proprietary LLMs and minimizes costs.

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Zewei Sun, Shujian Huang, Xinyu Dai, Jiajun Chen

Machine Translation

Challenge:	Recent studies show that the attention heads in Transformer are not equal.
Approach:	They propose a masking method to mask attention heads in Transformer . they empirically validate the inequality and propose 'head mask' method to avoid bottleneck .
Outcome:	The proposed masking method improves translation performance on multiple languages . it can be used to remove a small subset of heads without affecting performance .

Employing Discourse Coherence Enhancement to Improve Cross-Document Event and Entity Coreference Resolution (2025.acl-long)

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Xinyu Chen, Peifeng Li, Qiaoming Zhu

MSVBench: Towards Human-Level Evaluation of Multi-Shot Video Generation (2026.findings-acl)

Challenge:	Existing work on cross-document coreference resolution focuses on within-document events and entities, but cross-doc mentions lack such critical contexts.
Approach:	They propose a task to enhance the discourse coherence between two cross-document mentions by adding coherent texts to a document to form a new coherent document.
Outcome:	The proposed method outperforms state-of-the-art baselines on three popular datasets.

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Haoyuan Shi, Yunxin li, Nanhao Deng, Zhenran Xu, Xinyu Chen, Longyue Wang, Baotian Hu, Min Zhang

How to Enhance Causal Discrimination of Utterances: A Case on Affective Reasoning (2023.emnlp-main)

Challenge:	Existing evaluation methods for complex multi-shot video are anchored to single-shot paradigms, lacking comprehensive story assets and cross-shot metrics.
Approach:	They propose a framework that synergizes the high-level semantic reasoning of Large Multimodal Models with the fine-grained perceptual rigor of domain-specific expert models.
Outcome:	The proposed framework synergizes the high-level semantic reasoning of Large Multimodal Models with the fine-grained perceptual rigor of domain-specific expert models.

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Hang Chen, Xinyu Yang, Jing Luo, Wenjing Zhu

Representation Learning

Challenge:	Existing models excel at capturing semantic correlations within utterance embeddings but fail to determine specific causal relationships.
Approach:	They propose to incorporate i.i.d. noise terms into conversation process to build a structural causal model . they propose to use unstructured conversation data to facilitate deep learning .
Outcome:	The proposed approach can be implemented in unstructured conversation data and a synthetic dataset that includes i.i.d. noise.

EFUF: Efficient Fine-Grained Unlearning Framework for Mitigating Hallucinations in Multimodal Large Language Models (2024.emnlp-main)

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Shangyu Xing, Fei Zhao, Zhen Wu, Tuo An, Weihao Chen, Chunhui Li, Jianbing Zhang, Xinyu Dai

Do Deep Neural Nets Display Human-like Attention in Short Answer Scoring? (2022.naacl-main)

Challenge:	Existing methods to eliminate hallucinations require expensive human annotation . hallucination in multimodal large language models poses unique challenges for current research .
Approach:	They propose a fine-grained unlearning framework that performs gradient ascent to eliminate hallucinations without paired data.
Outcome:	The proposed method reduces hallucinations while preserving quality with modest computational overhead.

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Zijie Zeng, Xinyu Li, Dragan Gasevic, Guanliang Chen

Challenge:	DL-based graders often lack the ability to explain and justify how a prediction is made, which decreases their trustworthiness and hinders educators from embracing them in practice.
Approach:	They conducted a user study to determine whether DL-based graders align with human grader . they also ran a randomized controlled experiment to explore the impact of highlighting important words detected by DL grader.
Outcome:	The proposed method enables human graders to identify important words when marking short answer questions.

Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents (2023.emnlp-main)

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Weiwei Sun, Lingyong Yan, Xinyu Ma, Shuaiqiang Wang, Pengjie Ren, Zhumin Chen, Dawei Yin, Zhaochun Ren

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

Challenge:	Existing work utilizes generative LLMs for Information Retrieval (IR) rather than direct passage ranking.
Approach:	They investigate generative LLMs such as ChatGPT and GPT-4 for relevance ranking in IR and use a test set to verify the model’s ability to rank unknown knowledge.
Outcome:	The proposed model outperforms a 3B supervised model on the BEIR benchmark.

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

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

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

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

Structure-Unified M-Tree Coding Solver for Math Word Problem (2022.emnlp-main)

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

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Bin Wang, Jiangzhou Ju, Yang Fan, Xinyu Dai, Shujian Huang, Jiajun Chen

Numerical Reasoning

Challenge:	Existing models that take into account the binary tree structure of mathematical expressions have achieved better performance, but the output space is non-deterministic.
Approach:	They propose a Structure-Unified M-Tree Coding Solver which applies a tree with any M branches to unify the output structures.
Outcome:	The proposed model outperforms several state-of-the-art models under similar experimental conditions and performs much better under low-resource conditions.

Quantifying Semantic Emergence in Language Models (2025.acl-long)

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Hang Chen, Xinyu Yang, Jiaying Zhu, Wenya Wang

PreAlign: Boosting Cross-Lingual Transfer by Early Establishment of Multilingual Alignment (2024.emnlp-main)

Challenge:	Existing evaluation methods for large language models (LLMs) focus on coarse-grained text, not providing interpretations for the behavior of finergrained tokens.
Approach:	They propose a quantitative metric to measure large language models’ ability to extract semantics from input tokens.
Outcome:	The proposed metric compares the entropy reduction observed for a sequence of tokens and individual tokens.

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Jiahuan Li, Shujian Huang, Aarron Ching, Xinyu Dai, Jiajun Chen

Multilinguality

Challenge:	Large language models exhibit reasonable multilingual abilities, despite predominantly English-centric pretraining.
Approach:	They propose a framework that establishes multilingual alignment prior to language model pretraining and preserves this alignment using a code-switching strategy during pretraining.
Outcome:	Experiments in a synthetic English to English-Clone setting show that PreAlign outperforms standard multilingual joint training in language modeling, zero-shot cross-lingual transfer, and cross-linguistic knowledge application.

A Reinforced Generation of Adversarial Examples for Neural Machine Translation (2020.acl-main)

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Wei Zou, Shujian Huang, Jun Xie, Xinyu Dai, Jiajun Chen

Analyzing the Intensity of Complaints on Social Media (2022.findings-naacl)

Challenge:	Neural machine translation systems fail on less decent inputs, which may harm the credibility of these systems.
Approach:	They propose a paradigm that generates adversarial examples using reinforcement learning to expose pitfalls for a given performance metric.
Outcome:	The proposed paradigm produces stable attacks with meaning-preserving adversarial examples.

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Ming Fang, Shi Zong, Jing Li, Xinyu Dai, Shujian Huang, Jiajun Chen

Cross-Lingual Transfer

Challenge:	Prior studies on identifying the existence or the type of complaints focus on building automatic classification models for identifying complaints.
Approach:	They propose to measure the intensity of complaints from text using Best-Worst Scaling method to estimate the popularity of posts on social media.
Outcome:	The proposed model can estimate the popularity of complaints on social media with best-worst scaling (BWS) method.

Merge then Realign: Simple and Effective Modality-Incremental Continual Learning for Multimodal LLMs (2025.emnlp-main)

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Dingkun Zhang, Shuhan Qi, Xinyu Xiao, Kehai Chen, Xuan Wang

When "Correct" Is Not Safe: Can We Trust Functionally Correct Patches Generated by Code Agents? (2026.acl-long)

Challenge:	Recent advances in Multimodal Large Language Models (MLLMs) have enhanced their versatility as they integrate a growing number of modalities.
Approach:	They propose a simple MCL paradigm that addresses forgetting and misalignment . they propose 'MErge then ReAlign' to extend existing models to more modalities .
Outcome:	The proposed paradigm is easy to deploy and highly reusable in the MLLM community.

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Yibo Peng, James Song, Lei Li, Xinyu Yang, Mihai Christodorescu, Ravi Mangal, Corina S. Pasareanu, Haizhong Zheng, Beidi Chen

Task-Aware Resolution Optimization for Visual Large Language Models (2025.emnlp-main)

Challenge:	Code agents are increasingly trusted to autonomously fix bugs on platforms such as GitHub, yet their security evaluation focuses on functional correctness.
Approach:	They propose to attack functionally correct yet vulnerable (FCV) patches by combining multi-turn reasoning with tool invocation and environment interaction.
Outcome:	The proposed FCV-Attack achieves an attack success rate of 40.7% on GPT-5 Mini + OpenHands.

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Weiqing Luo, Zhen Tan, Yifan Li, Xinyu Zhao, Kwonjoon Lee, Behzad Dariush, Tianlong Chen

Visual Data in NLP

Challenge:	Existing visual large language models pre-assume a fixed resolution for downstream tasks, leading to sub-optimal performance.
Approach:	They propose a formula to determine the optimal resolution for a given vision-language task . they then propose 'parameter-efficient' fine-tuning technique to extend the visual input resolution .
Outcome:	The proposed method is based on rigorous experiments on vision-language tasks.

Is the Attention Matrix Really the Key to Self-Attention in Multivariate Long-Term Time Series Forecasting? (2026.acl-long)

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Xinyu Li, Kexi Chen, Jiajie Shen, Ying Zheng, Hong Lu, Jin Zhao, Xin Wang

GRNFormer: A Biologically-Guided Framework for Integrating Gene Regulatory Networks into RNA Foundation Models (2025.findings-acl)

Challenge:	In multivariate long-term time series forecasting, it is widely believed that the effectiveness of self-attention arises from its attention matrix.
Approach:	They propose a multi-branch MLP that isolates the ‘multi-brain mapping with element-wise operation’ structure from the Transformer and shows that it achieves competitive performance.
Outcome:	The proposed model outperforms three classic and three latest Transformer models and shows that it achieves competitive performance.

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Mufan Qiu, Xinyu Hu, Fengwei Zhan, Sukwon Yun, Jie Peng, Ruichen Zhang, Bhavya Kailkhura, Jiekun Yang, Tianlong Chen

Structured Data in NLP

Challenge:	Foundation models for single-cell RNA sequencing ignore biological prior knowledge encoded in gene regulatory relationships and fail to leverage multi-omics signals.
Approach:	They propose a framework that integrates multi-scale gene regulatory networks into RNA foundation model training.
Outcome:	The proposed framework improves on state-of-the-art models on three downstream tasks . it integrates multi-scale gene regulatory networks (GRNs) from multi-omics data into training .

OPT-BENCH: Evaluating the Iterative Self-Optimization of LLM Agents in Large-Scale Search Spaces (2026.findings-acl)

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Xiaozhe Li, Jixuan Chen, Xinyu Fang, Shengyuan Ding, Haodong Duan, Qingwen Liu, Kai Chen

Information Retrieval

Challenge:	Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning and tool use, but their ability to continuously refine solutions in response to dynamic environmental feedback remains underexplored.
Approach:	They propose a benchmark to evaluate self-improvement capabilities in large-scale search spaces by combining 20 machine learning tasks with 10 classic NP-hard problems.
Outcome:	The proposed framework emulates human-like cognitive adaptation and operates via a general perception–memory–reasoning loop, iteratively refining solutions based on environmental feedback.

MTR-Suite: A Framework for Evaluating and Synthesizing Conversational Retrieval Benchmarks (2026.acl-long)

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Junhao Ruan, Abudukeyumu Abudula, Bei Li, Yongjing Yin, Xinyu Liu, Kechen Jiao, Xin Chen, Jingang Wang, Xunliang Cai, Tong Xiao, JingBo Zhu

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

Challenge:	Existing conversational retrieval benchmarks suffer from costly, sparse human annotation or rigid, unnatural automated heuristics.
Approach:	They propose a framework for auditing, synthesizing, and benchmarking conversational retrieval.
Outcome:	The proposed framework is based on three LLM-based auditors and a multi-agent system . it mimics production-style challenges (hard topic switching, verbosity) and offers superior discriminative power.

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

Explainability & Interpretability in NLP

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

Incorporating Temporal Coherence to Cross-Document Event Coreference Resolution (2026.acl-long)

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Xinyu Chen, Peifeng Li, Qiaoming Zhu

Learning Flexible Large Multimodal Models with Arbitrary Modality Combinations (2026.findings-acl)

Challenge:	Existing approaches focus on enhancing semantic coherence between event mentions, but they overlook the critical aspect of temporal coherency.
Approach:	They propose a Temporal Cohorence-driven event coreference framework that explicitly models temporal constraints by constructing a temporal event graph and a GNN to resolve conflicts.
Outcome:	Experiments on the ECB+, GVC, WEC, and ECb+META datasets show that CohTP outperforms state-of-the-art methods.

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Xinyu Zhao, Kangqi Ni, Jie Peng, Ang Li, Tianlong Chen

Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet Extraction (2023.findings-acl)

Challenge:	Multimodal Large Language Models (MLLMs) have potential for cross-modal understanding . but extending MLLM to handle diverse modalities introduces two challenges .
Approach:	They propose a dual-stage compression mechanism to reduce the number of modality tokens per modality and condense it into a single, compact token sequence.
Outcome:	Experiments show that Flex-M3 outperforms its counterpart trained on only full-modality data.

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Ting Xu, Huiyun Yang, Zhen Wu, Jiaze Chen, Fei Zhao, Xinyu Dai

Sentiment Analysis

Challenge:	Existing ASTE datasets are limited in their ability to represent real-world scenarios, hindering progress in this area.
Approach:	They propose a new ASTE dataset that is manually annotated to better fit real-world scenarios by providing more diverse and realistic reviews.
Outcome:	The proposed dataset is manually annotated to better fit real-world scenarios.

Dialogue is Better Than Monologue: Instructing Meidcal LLMs via Strategic Conversations (2026.findings-eacl)

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Zijie Liu, Xinyu Zhao, Jie Peng, Jinhao Duan, Zhuangdi Zhu, Qingyu Chen, Kaidi Xu, Xia Hu, Tianlong Chen

LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification (2021.acl-long)

Challenge:	Existing tuning methods for medical AI models are monologue-based . existing benchmarks are based on licensing exams or research articles .
Approach:	They propose a benchmark to expose limitations of monologue-based tuning for medical AI models . they use a large dialogue dataset to capture stepwise diagnostic reasoning .
Outcome:	The proposed model outperforms monologue-tuned models on a medical question answering task and improves accuracy on standard medical QA benchmarks.

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Xinyu Zuo, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Weihua Peng, Yuguang Chen

Cross-Document Event Coreference Resolution on Discourse Structure (2023.emnlp-main)

Challenge:	Existing methods for event causality identification (ECI) rely on annotated training data.
Approach:	They propose a method to augment training data for event causality identification by iteratively generating new examples and classifying event causalities in a dual learning framework.
Outcome:	The proposed method outperforms existing methods on EventStoryLine and Causal-TimeBank.

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Xinyu Chen, Sheng Xu, Peifeng Li, Qiaoming Zhu

Rhythm Controllable and Efficient Zero-Shot Voice Conversion via Shortcut Flow Matching (2025.acl-long)

Challenge:	Experimental results show that our proposed model outperforms several baselines and achieves the competitive performance with the start-of-the-art baselines.
Approach:	They propose to use discourse rhetorical structure constructor to construct tree structures to represent documents and a multi-layer perceptron to capture similarities of event mention pairs.
Outcome:	The proposed model outperforms baselines and achieves competitive performance with the start-of-the-art baselines.

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Jialong Zuo, Shengpeng Ji, Minghui Fang, Mingze Li, Ziyue Jiang, Xize Cheng, Xiaoda Yang, Chen Feiyang, Xinyu Duan, Zhou Zhao

Revisit Self-Debugging with Self-Generated Tests for Code Generation (2025.acl-long)

Challenge:	Existing methods focus on disentangling speakers and content, while others focus on preserving the source's prosody.
Approach:	They propose a rhythm-controllable and efficient zero-shot voice conversion model that transforms the source speaker’s timbre into an unseen one while retaining speech content.
Outcome:	The proposed model adapts the linguistic content duration to the desired speaking style, facilitating the transfer of the target speaker’s rhythm.

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Xiancai Chen, Zhengwei Tao, Kechi Zhang, Changzhi Zhou, Xinyu Zhang, Wanli Gu, Yuanpeng He, Mengdi Zhang, Xunliang Cai, Haiyan Zhao, Zhi Jin

Programming Languages in NLP

Challenge:	Large language models (LLMs) have made significant advances in code generation, but they still face challenges when tackling complex programming tasks beyond their basic capabilities.
Approach:	They propose to integrate self-generated tests into the code generation process . they propose to use post-execution and in-exection self-debugging to mitigate test bias .
Outcome:	The proposed method improves the performance of large language models in code generation tasks by leveraging execution feedback from tests.

UniMoE-Audio: Unified Speech and Music Generation with Dynamic-Capacity Mixture-of-Experts (2026.acl-long)

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Zhenyu Liu, Yunxin li, Xuanyu Zhang, Qixun Teng, Shenyuan Jiang, Xinyu Chen, Haoyuan Shi, Haolan Chen, Fanbo Meng, Mingjun Zhao, Yu Xu, Yancheng He, Baotian Hu, Haizhou Li, Min Zhang

Beyond the Surface: Measuring Self-Preference in LLM Judgments (2025.emnlp-main)

Challenge:	Recent advances in unified multimodal models indicate a clear trend towards comprehensive content generation.
Approach:	They propose a unified speech and music generation model built upon a novel framework . they propose specialized MoE architectures and curated training strategies to tackle data imbalances .
Outcome:	The proposed model achieves state-of-the-art performance on major speech and music generation benchmarks.

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Zhi-Yuan Chen, Hao Wang, Xinyu Zhang, Enrui Hu, Yankai Lin

Debiasing In-Context Learning by Instructing LLMs How to Follow Demonstrations (2024.findings-acl)

Challenge:	Existing methods measure self-preference bias by comparing the scores a judge model assigns to its own responses with those assigned to other models.
Approach:	They propose to use gold judgments as proxies for the actual quality of responses . they propose to measure self-preference bias as the difference between the judge model's own and other models' scores .
Outcome:	The proposed method can assess self-preference bias across large language models . it uses gold judgments as proxies for the ground truth scores of the judge model .

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Lvxue Li, Jiaqi Chen, Xinyu Lu, Yaojie Lu, Hongyu Lin, Shuheng Zhou, Huijia Zhu, Weiqiang Wang, Zhongyi Liu, Xianpei Han, Le Sun

SIV-Bench: A Video Benchmark for Social Interaction Understanding and Reasoning (2026.findings-acl)

Challenge:	In-context learning (ICL) has gained considerable attention due to its data efficiency and task adaptability.
Approach:	They propose to de-biase demonstration bias in in-context learning by focusing on semantic ambiguity induced by demonstrations and reducing the semantic hazard.
Outcome:	The proposed methods significantly improve performance on six datasets.

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Fanqi Kong, Weiqin Zu, Xinyu Chen, Yaodong Yang, Song-Chun Zhu, Xue Feng

TransFace: Unit-Based Audio-Visual Speech Synthesizer for Talking Head Translation (2024.findings-acl)

Challenge:	Existing benchmarks for Multimodal Large Language Models (MLLMs) have been lacking due to the rich nature of social interaction.
Approach:	They propose a video benchmark to evaluate MLLMs' capabilities across social scene understanding, social state reasoning, and social dynamics prediction.
Outcome:	The proposed benchmarks evaluate MLLMs' capabilities across social scene understanding, social state reasoning, and social dynamics prediction tasks.

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Xize Cheng, Rongjie Huang, Linjun Li, Zehan Wang, Tao Jin, Aoxiong Yin, Chen Feiyang, Xinyu Duan, Baoxing Huai, Zhou Zhao

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

Challenge:	Existing methods for talking head translation rely on cascading, resulting in delays and cascadic errors.
Approach:	They propose a model for talking head translation, TransFace, which can translate audio-visual speech into audio-visual speech in other languages.
Outcome:	The proposed model can translate audio-visual speech into audio-visual speech in other languages.

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

Non-Autoregressive Translation by Learning Target Categorical Codes (2021.naacl-main)

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

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Yu Bao, Shujian Huang, Tong Xiao, Dongqi Wang, Xinyu Dai, Jiajun Chen

Text Generation

Challenge:	Existing non-autoregressive text generation models still fall behind in translation quality . authors propose a model that learns implicitly categorical codes as latent variables .
Approach:	They propose a non-autoregressive Transformer model that implicitly categorizes latent variables into decoding . they find it improves translation quality by introducing more informative decoder inputs .
Outcome:	The proposed model achieves comparable or better performance in machine translation tasks than strong baselines.

Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment (P18-1)

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Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, Ivan Marsic

Sentiment Analysis

Challenge:	Existing approaches to classify human affect and subjective information from multiple data sources are limited by the lack of high-level feature associations.
Approach:	They propose a hierarchical multimodal architecture with attention and word-level fusion to classify utterance-level sentiment and emotion from text and audio data.
Outcome:	The proposed model outperforms state-of-the-art approaches on published datasets and visualizes and interprets synchronized attention over modalities.

Boosting Data Utilization for Multilingual Dense Retrieval (2025.emnlp-main)

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Chao Huang, Fengran Mo, Yufeng Chen, Changhao Guan, Zhenrui Yue, Xinyu Wang, Jinan Xu, Kaiyu Huang

Information Retrieval

Challenge:	Existing studies focus on fine-tuning multilingual dense retrieval models, but data scarcity for low-resource languages makes it difficult to align representations in a shared vector space.
Approach:	They propose to obtain high-quality hard negative samples and effective mini-batch data to boost data utilization for multilingual dense retrieval by obtaining high-quality negative samples.
Outcome:	The proposed method outperforms existing baselines on a multilingual retrieval benchmark, MIRACL, with 16 languages.

MedVerse: Efficient and Reliable Medical Reasoning via DAG-Structured Parallel Execution (2026.acl-long)

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Jianwen Chen, Xinyu Yang, Peng Xia, Arian Azarang, Yueh Z Lee, Gang Li, Hongtu Zhu, Yun Li, Beidi Chen, Huaxiu Yao

Green & Sustainable NLP

Challenge:	Recent advances in large reasoning models have broadened the capabilities of medical artificial intelligence.
Approach:	They propose a reasoning framework for complex medical inference that reformulates medical reasoning as a parallelizable directed acyclic graph process based on Petri Net theory.
Outcome:	The proposed reasoning framework improves strong general-purpose LLMs by up to 8.9%.

A Layer-wise Analysis of Supervised Fine-Tuning (2026.acl-long)

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Qinghua Zhao, Xueling Gong, Xinyu Chen, Zhongfeng Kang, Xinlu Li

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

Challenge:	Existing methods for fine-tuning ignore depth-dependent heterogeneity of instruction-following . a critical gap remains in understanding where these changes occur across the model's depth and which layers are essential for instruction- following.
Approach:	They propose a method which selectively updates critical intermediate layers . they show that effective alignment is architecturally localized rather than distributed .
Outcome:	The proposed method outperforms standard LoRA up to 10.2% on GSM8K with reduced parameter overhead.

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

Forge: Quality-Aware Reinforcement Learning for NP-Hard Optimization in LLMs (2026.findings-acl)

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

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Xiaozhe Li, Xinyu Fang, Shengyuan Ding, Yang Li, Linyang Li, Haodong Duan, Qingwen Liu, Kai Chen

Reasoning

Challenge:	Existing benchmarks focus on correctness, overlooking optimality . large language models excel at math, coding, logic and puzzles .
Approach:	They propose a framework for training and evaluating Large Language Models on NP-hard optimization problems through quality-aware RLVR.
Outcome:	The proposed framework outperforms existing benchmarks on math, coding, logic and puzzles.

Meta-LMTC: Meta-Learning for Large-Scale Multi-Label Text Classification (2021.emnlp-main)

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Ran Wang, Xi’ao Su, Siyu Long, Xinyu Dai, Shujian Huang, Jiajun Chen

Demystify the Role of Memory in Machine Learning Engineering Agents (2026.findings-acl)

Challenge:	Large-scale multi-label text classification tasks often face long-tailed label distributions, where many labels have few or even no training instances.
Approach:	They propose a meta-learning approach that incorporates the objective of adapting to new low-resource tasks into the meta-Learning phase.
Outcome:	The proposed approach achieves state-of-the-art against strong baselines and can still enhance powerful BERTlike models.

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Xinyu Zhao, Junpeng Wang, Yuzhong Chen, Menghai Pan, Chin-Chia Michael Yeh, Jiarui Sun, Yan Zheng, Mahashweta Das, Tianlong Chen

Challenge:	Unlike short, reactive exchanges, MLE agents solve tasks through cycles of experimentation and improvement where past errors can inform future success.
Approach:	They propose a dynamic coding memory that captures and reuses debugging experiences and integrates it into two representative agent paradigms.
Outcome:	The proposed agent model captures and reuses debugging experiences and integrates it into two agent paradigms.

Linguistic Minimal Pairs Elicit Linguistic Similarity in Large Language Models (2025.coling-main)

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Xinyu Zhou, Delong Chen, Samuel Cahyawijaya, Xufeng Duan, Zhenguang Cai

BrowseComp-Plus: A Fair and Disentangled Evaluation Benchmark for Deep Search Agents (2026.acl-long)

Challenge:	a new analysis leverages linguistic minimal pairs to probe the internal linguistic representations of Large Language Models (LLMs).
Approach:	They propose to use linguistic minimal pairs to probe the internal linguistic representations of Large Language Models (LLMs).
Outcome:	The proposed analysis reveals that linguistic similarity is significantly influenced by training data exposure, leading to higher cross-LLM agreement in higher-resource languages.

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Zijian Chen, Xueguang Ma, Shengyao Zhuang, Ping Nie, Kai Zou, Sahel Sharifymoghaddam, Andrew Liu, Joshua Green, Kshama Patel, Ruoxi Meng, Mingyi Su, Yanxi Li, Haoran Hong, Xinyu Shi, Xuye Liu, Hosna Oyarhoseini, Nandan Thakur, Crystina Zhang, Luyu Gao, Wenhu Chen, Jimmy Lin

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

Challenge:	Existing benchmarks for deep search agents rely on blackbox web search APIs . dynamic and opaque web APIs hinder reproducibility and fair comparisons - authors .
Approach:	They propose a benchmark that employs a fixed corpus for controlled retrieval for deep search agents.
Outcome:	The new benchmark shows that agents that combine large language models with retrieval tools excel at complex, reasoning-intensive queries.

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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

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

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

Hybrid Attention based Multimodal Network for Spoken Language Classification (C18-1)

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

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Yue Gu, Kangning Yang, Shiyu Fu, Shuhong Chen, Xinyu Li, Ivan Marsic

latent-GLAT: Glancing at Latent Variables for Parallel Text Generation (2022.acl-long)

Challenge:	Using linguistic content and vocal characteristics for multimodal deep learning is difficult for computers to interpret human meaning .
Approach:	They propose a deep multimodal network with feature attention and modality attention to classify utterance-level speech data.
Outcome:	The proposed system achieves state-of-the-art or competitive results on three published multimodal datasets.

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Yu Bao, Hao Zhou, Shujian Huang, Dongqi Wang, Lihua Qian, Xinyu Dai, Jiajun Chen, Lei Li

When is Char Better Than Subword: A Systematic Study of Segmentation Algorithms for Neural Machine Translation (2021.acl-short)

Challenge:	Recent advances in text generation have limited applications due to multimodality problem.
Approach:	They propose a method which uses latent variables to capture word categorical information and invoke an advanced curriculum learning technique to overcome multi-modality problem.
Outcome:	The proposed method outperforms strong baselines without an autoregressive model, which further broadens the application scenarios of the parallel decoding paradigm.

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Jiahuan Li, Yutong Shen, Shujian Huang, Xinyu Dai, Jiajun Chen

Machine Translation

Challenge:	Subword segmentation algorithms can produce sub-optimal segmentation when the target language is rich in morphological changes or there is not enough data for learning compact composition rules.
Approach:	They compare character-based and subword-based neural machine translation systems . they find character-driven models are better at handling morphological phenomena .
Outcome:	The character-based models are better at handling morphological phenomena, generating rare and unknown words, and more suitable for transferring to unseen domains.

A Multi-Modal Context Reasoning Approach for Conditional Inference on Joint Textual and Visual Clues (2023.acl-long)

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Yunxin Li, Baotian Hu, Chen Xinyu, Yuxin Ding, Lin Ma, Min Zhang

Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks (2021.acl-long)

Challenge:	Existing methods for conditional inference on joint textual and visual clues lack multimodal context reasoning capability.
Approach:	They propose a multi-modal context reasoning approach that embeds textual semantics and objective image information into the pretrained language model to perform context reasoning.
Outcome:	The proposed approach improves on two data sets and shows 4.8% gain on the PMR.

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Pengfei Cao, Xinyu Zuo, Yubo Chen, Kang Liu, Jun Zhao, Yuguang Chen, Weihua Peng

Structured Data in NLP

Challenge:	Existing methods for identifying causal relations of events are limited . Existing approaches cannot handle well the problem, especially in the condition of lacking training data.
Approach:	They propose a Latent Structure Induction Network to integrate external structural knowledge into a causality reasoning task.
Outcome:	The proposed approach outperforms existing state-of-the-art methods on two widely used datasets.

Redundancy Principles for MLLMs Benchmarks (2025.acl-long)

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Zicheng Zhang, Xiangyu Zhao, Xinyu Fang, Chunyi Li, Xiaohong Liu, Xiongkuo Min, Haodong Duan, Kai Chen, Guangtao Zhai

Sketch-Driven Regular Expression Generation from Natural Language and Examples (2020.tacl-1)

Challenge:	Rapid growth of Multi-modality Large Language Models has led to significant redundancy among benchmarks.
Approach:	They propose a framework to improve MLLM benchmark design by identifying redundancy at three levels: dimension, instance, and cross-benchmark redundancies.
Outcome:	The proposed framework streamlines evaluations and enhances reliability.

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Xi Ye, Qiaochu Chen, Xinyu Wang, Isil Dillig, Greg Durrett

Text Generation

Challenge:	Recent systems for converting natural language descriptions into regexes have achieved some success, but typically deal with short, formulaic text and can only produce simple regexe.
Approach:	They propose a framework for regex synthesis in a context where both natural language and examples are available.
Outcome:	The proposed framework achieves state-of-the-art on two prior datasets and a real-world dataset, which existing neural systems completely fail on.

Enhancing LLM-Based Social Bot via an Adversarial Learning Framework (2025.emnlp-main)

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Fanqi Kong, Xiaoyuan Zhang, Xinyu Chen, Yaodong Yang, Song-Chun Zhu, Xue Feng

Looking Beyond the One: Operationalizing and Eliciting Visual Ambiguity in VLLMs (2026.acl-long)

Challenge:	Social media platforms provide an ideal testbed for large language models that exhibit human-like behavior.
Approach:	They propose an LLM-based social **Bot that enhances human-like generative capabilities through an adversarial learning framework.
Outcome:	The proposed framework generates human-like content aligned with diverse user profiles . it exhibits strong social responsiveness, more accurately modeling opinion dynamics .

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Yuchong Chen, Bowei Zou, Yuhan Chen, Yifan Fan, Xinyu Li, Shujun Cao, Yu Hong

Visual Data in NLP

Challenge:	Visual question answering systems typically collapse ambiguity, committing to a single interpretation during decoding and evaluation.
Approach:	They operationalize ambiguity as the existence of multiple answer-supporting regions in an image . they show that ambiguities are already encoded in their internal representations .
Outcome:	The proposed approach makes ambiguity observable without exhaustive annotations . ambiguities are already encoded in models, but not reliably expressed in outputs despite hidden states .

GuideLLM: Exploring LLM-Guided Conversation with Applications in Autobiography Interviewing (2025.naacl-long)

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Jinhao Duan, Xinyu Zhao, Zhuoxuan Zhang, Eunhye Grace Ko, Lily Boddy, Chenan Wang, Tianhao Li, Alexander Rasgon, Junyuan Hong, Min Kyung Lee, Chenxi Yuan, Qi Long, Ying Ding, Tianlong Chen, Kaidi Xu

VideoVista-CulturalLingo: 360° Horizons-Bridging Cultures, Languages, and Domains in Video Comprehension (2025.acl-long)

Challenge:	Large Language Models (LLMs) have demonstrated their effectiveness in human-guided dialogues, but tasks in the real world are more complex and require greater autonomy from LLMs.
Approach:	They propose to characterize LLM-guided conversation into three fundamental components: Goal Navigation, Context Management, Empathetic Engagement and implement an interviewing environment for the evaluation of LLMs.
Outcome:	The proposed LLM outperforms baseline LLMs in interviewing quality and autobiography generation quality.

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Xinyu Chen, Yunxin Li, Haoyuan Shi, Baotian Hu, Wenhan Luo, Yaowei Wang, Min Zhang

Visual Data in NLP

Challenge:	Existing video evaluation benchmarks focus on a single language, typically English, and feature videos rooted in Western cultural contexts.
Approach:	They propose a video evaluation benchmark designed to bridge cultural, linguistic, and domain divide in video comprehension.
Outcome:	The proposed video evaluation benchmark bridges cultural, linguistic, and domain divides . existing benchmarks only feature videos from YouTube, Shutterstock, or established video datasets based on cultural diversity .

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

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

Text Generation

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

Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement (2021.findings-acl)

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Xinyu Zuo, Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Weihua Peng, Yuguang Chen

Representation Learning

Challenge:	Existing methods for event causality identification (ECI) rely on labeled data, but the scale of annotated datasets is limited.
Approach:	They propose a self-supervised framework to learn context-specific causal patterns from external causal statements and adopt a contrastive transfer strategy to incorporate the learned context- specific causal patterns into the target ECI model.
Outcome:	The proposed method significantly outperforms existing methods on EventSto-ryLine and Causal-TimeBank (+2.0 and +3.4 points on F1 value respectively).

Dialogue State Tracking with Explicit Slot Connection Modeling (2020.acl-main)

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Yawen Ouyang, Moxin Chen, Xinyu Dai, Yinggong Zhao, Shujian Huang, Jiajun Chen

Dialogue Systems & Conversational Agents

Challenge:	Existing methods to track dialogue state are lacking in multi-domain scenarios.
Approach:	They propose a model that explicitly considers slot correlations across domains . they propose ellipsis and reference to express values that have been mentioned by slots from other domains.
Outcome:	The proposed model outperforms existing models on multi-domain datasets and achieves state-of-the-art performance.

MATCH: Modulating Attention via In-Context Retrieval for Long-Context Transformers (2026.acl-long)

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Linrui Ma, Chun Hei Lo, Xinyu Wang, Peng Lu, Xihao Yuan, Hanting Chen, Kai Han, Xinghao Chen, Chengjun Zhan, Hanlin xu, Yichun Yin, Lifeng Shang, Feng Wen, Boxing Chen, Yufei Cui

Dr3: Ask Large Language Models Not to Give Off-Topic Answers in Open Domain Multi-Hop Question Answering (2024.lrec-main)

Challenge:	Existing approaches to improve efficiency often enforce rigid structural constraints such as local attention windows.
Approach:	They propose a framework that augments sparse-attention mechanisms with dynamically integrated in-context information through an efficient retrieval system.
Outcome:	Empirical results show that MATCH significantly improves the performance of sparse-attention models on synthetic and real-world natural-language tasks.

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Yuan Gao, Yiheng Zhu, Yuanbin Cao, Yinzhi Zhou, Zhen Wu, Yujie Chen, Shenglan Wu, Haoyuan Hu, Xinyu Dai

Question Answering

Challenge:	Open Domain Multi-Hop Question Answering (ODMHQA) is one of the most challenging tasks in Natural Language Processing (NLP)
Approach:	They propose a mechanism that leverages the intrinsic capabilities of Large Language Models to judge whether the generated answers are off-topic.
Outcome:	The proposed method reduces the occurrence of off-topic answers by nearly 13%, improving the performance in Exact Match (EM) by nearly 3% compared to the baseline method without the Dr3 mechanism.

Polymorphic Universal Transformer (2026.acl-long)

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Yilong Chen, Zitian Gao, Yihao Xiao, Jason Klein Liu, Xinyu Yang, Yifan Luo, Haoming Luo, Zhengmao Ye, Tingwen Liu, Ran Tao, Bryan Dai

ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs (2024.findings-emnlp)

Challenge:	Compute Distribution Skew is a pathological phenomenon in ultra-deep recurrent models . it causes over-smoothing, representation rank collapse, and degraded reasoning performance.
Approach:	They propose a dynamic architecture that redefines recursive computation by decoupling parameter count from depth.
Outcome:	The proposed model significantly improves representation rank and reasoning robustness while reducing computation by 64.7%.

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Jingming Zhuo, Songyang Zhang, Xinyu Fang, Haodong Duan, Dahua Lin, Kai Chen

HoLLMwood: Unleashing the Creativity of Large Language Models in Screenwriting via Role Playing (2024.findings-emnlp)

Challenge:	Recent research has neglected instances-level prompt variations and their implications on subjective evaluations.
Approach:	They propose a framework to evaluate and comprehend prompt sensitivity in large language models.
Outcome:	The proposed framework evaluates and comprehends prompt sensitivity in large language models.

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Jing Chen, Xinyu Zhu, Cheng Yang, Chufan Shi, Yadong Xi, Yuxiang Zhang, Junjie Wang, Jiashu Pu, Tian Feng, Yujiu Yang, Rongsheng Zhang

Hyperlink-induced Pre-training for Passage Retrieval in Open-domain Question Answering (2022.acl-long)

Challenge:	Generative AI has demonstrated unprecedented creativity in the field of computer vision, yet such phenomena have not been observed in the realm of literary creation.
Approach:	They propose a framework for unleashing the creativity of large language models (LLMs) they assign LLMs to different roles involved in real-world scenario, they write .
Outcome:	The proposed framework outperforms baselines in terms of coherence, relevance, interestingness and overall quality on automatically generated screenplays.

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Jiawei Zhou, Xiaoguang Li, Lifeng Shang, Lan Luo, Ke Zhan, Enrui Hu, Xinyu Zhang, Hao Jiang, Zhao Cao, Fan Yu, Xin Jiang, Qun Liu, Lei Chen

Energy-based Unknown Intent Detection with Data Manipulation (2021.findings-acl)

Challenge:	Existing methods to train dense passage retrieval have a large data gap between upstream and downstream relevance.
Approach:	They propose a method to pre-train the dense retriever with the text relevance induced by hyperlinks within Web documents.
Outcome:	The proposed method outperforms existing methods under different scenarios and in the open-domain question answering domain.

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Yawen Ouyang, Jiasheng Ye, Yu Chen, Xinyu Dai, Shujian Huang, Jiajun Chen

Sentiment Analysis

Challenge:	Unknown intent detection aims to identify the out-of-distribution (OOD) utterance whose intent has never appeared in the training set.
Approach:	They propose a framework to generate high-quality OOD utterances with importance weighTs (GOT) their framework is fine-tuned to detect out-of-distribution utterrances .
Outcome:	The proposed framework can achieve state-of-the-art results on two benchmark datasets.

OmniAlign-V: Towards Enhanced Alignment of MLLMs with Human Preference (2025.acl-long)

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Xiangyu Zhao, Shengyuan Ding, Zicheng Zhang, Haian Huang, Maosongcao Maosongcao, Jiaqi Wang, Weiyun Wang, Xinyu Fang, Wenhai Wang, Guangtao Zhai, Hua Yang, Haodong Duan, Kai Chen

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

Challenge:	Existing open-source multi-modal large language models (MLLMs) focus on enhancing foundational capabilities, leaving a significant gap in human preference alignment.
Approach:	They propose a dataset of 200K high-quality training samples featuring diverse images, complex questions, and varied response formats to improve MLLMs’ alignment with human preferences.
Outcome:	The proposed dataset of 200K high-quality training samples improves human preference alignment while maintaining or enhancing performance on standard VQA benchmarks.

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