Papers by Xin Su
CLEME2.0: Towards Interpretable Evaluation by Disentangling Edits for Grammatical Error Correction (2025.acl-long)
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Jingheng Ye, Zishan Xu, Yinghui Li, Linlin Song, Qingyu Zhou, Hai-Tao Zheng, Ying Shen, Wenhao Jiang, Hong-Gee Kim, Ruitong Liu, Xin Su, Zifei Shan
| Challenge: | Existing studies have focused on the interpretability of Grammatical Error Correction (GEC) evaluation metrics, but the interpretabilty of these metrics has been neglected. |
| Approach: | They propose a reference-based metric that describes four aspects of GEC systems: hit-correction, wrong-corrections, under-correcties, and over-corrects. |
| Outcome: | The proposed metric reveals critical qualities and locates drawbacks of GEC systems. |
Steering Away from Refusal: A Black-box Jailbreak Method Based on First-Token Distribution (2026.findings-acl)
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| Challenge: | Existing methods to analyze black-box jailbreaks lack direct optimization signals to refine adversarial prompts. |
| Approach: | They propose a distribution-jailbreak attack method that selects effective jailbreak templates and iteratively optimizes adversarial suffixes by maximizing the KL divergence from the standard refusal distribution. |
| Outcome: | The proposed method achieves state-of-the-art Attack Success Rate (ASR) on all tested open-source models and delivers over 94% ASR on GPT-4.1. |
Fusing Temporal Graphs into Transformers for Time-Sensitive Question Answering (2023.findings-emnlp)
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| Challenge: | Existing methods for extracting temporal information from text are not suitable for time-sensitive questions. |
| Approach: | They propose to use existing temporal information extraction systems to construct temporal graphs of events, times, and temporal relations in questions and documents. |
| Outcome: | The proposed method outperforms graph convolution-based approaches on SituatedQA and TimeQA. |
Bridging Subword Gaps in Pretrain-Finetune Paradigm for Natural Language Generation (2021.acl-long)
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| Challenge: | Existing methods to pretrain language models are limited by one-size-fits-all vocabulary . embeddings of mismatch tokens can be efficiently initialized in downstream tasks . |
| Approach: | They propose to extend pretrain-finetune pipeline with an embedding transfer step . plug-and-play embeddable generator is introduced to generate any input token . |
| Outcome: | The proposed approach allows for more efficient and better performed NLG models. |
Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation (2024.findings-naacl)
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| Challenge: | Parameter-efficient fine-tuning (PEFT) methods are important in low-resource language (LRL) Neural Machine Translation (NMT) but their practical effectiveness varies significantly across different languages. |
| Approach: | They evaluated the performance of 8 parameters-efficient fine-tuning methods with 15 architectures using the SacreBLEU score. |
| Outcome: | The Houlsby+Inversion adapter outperforms the baseline architectures in both in-domain and out-domain tests and the Houlson+Inverter achieves the best performance overall. |
Temporal Scaling Law for Large Language Models (2025.emnlp-main)
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Yizhe Xiong, Xiansheng Chen, Xin Ye, Hui Chen, Zijia Lin, Haoran Lian, Zhenpeng Su, Wei Huang, Jianwei Niu, Jungong Han, Guiguang Ding
| Challenge: | Existing studies have found that the test loss of LLMs scales as power-laws with model size, computational budget, and dataset size. |
| Approach: | They propose a concept of Temporal Scaling Law to study test loss of LLMs . they break down test loss into fine-grained token positions and develop a dynamic hyperbolic-law . |
| Outcome: | The proposed model predicts the test loss of LLMs as the training steps scale up. |
StoryMI: Steerable Multi-Agent Therapeutic Dialogue Generation (2026.findings-acl)
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| Challenge: | Motivational interviewing (MI) is a directive, client-centered counseling approach for eliciting clients' motivation for behavioral change. |
| Approach: | They propose a multi-LLM agent framework for controllable MI dialogue generation . therapist and client agents generate MI-coded utterances guided by MI codes . |
| Outcome: | The proposed framework can generate fluent dialogues with minimal intervention time and a high level of evaluation. |
SELECting over Tokens: Curating Pre-training Data at Scale via Token Classification (2026.acl-long)
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Xin Tong, Weidong Zhang, Jiaang Li, Haibin Chen, Shilei Liu, Langming Liu, Kangtao Lv, Yujin Yuan, Wenbo Su, Bo Zheng
| Challenge: | Existing pipelines rely on expert-crafted heuristic rules, which lack content-aware, fine-grained noise detection. |
| Approach: | They propose a framework that reframes data refinement as a highly efficient token classification task. |
| Outcome: | The proposed framework outperforms existing pipelines on benchmarks and is 2.5x faster at inference. |
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-Tuning (2025.emnlp-demos)
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Zhong Zhang, Yaxi Lu, Yikun Fu, Yupeng Huo, Shenzhi Yang, Yesai Wu, Han Si, Xin Cong, Haotian Chen, Yankai Lin, Xie Xie, Wei Zhou, Wang Xu, Zhou Su, Zhongwu Zhai, Xiaoming Liu, null Meiyudong, Jianming Xu, Hongyan Tian, Chongyi Wang, Chi Chen, Yuan Yao, Zhiyuan Liu, Maosong Sun
| Challenge: | Large language model agents have enabled GUI-based automation, but their deployment is limited by noisy data, poor generalization, and lack of support for non-English GUIs. |
| Approach: | They propose an 8B-parameter GUI agent built for robust and efficient on-device GUI interaction. |
| Outcome: | The proposed GUI agent achieves promising performance on five public benchmarks and proposed Chinese benchmark CAGUI. |
Compete to Complete: Co-opetition Adversarial Learning for Retrieval-Augmented Generation (2026.acl-long)
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| Challenge: | Existing approaches to reduce hallucination in large language models lack a robust mechanism for generating a generative model. |
| Approach: | They propose a framework that formulates retriever–generator training in RAG as a minimax game. |
| Outcome: | The proposed framework improves retrieval-augmented generation performance on seven benchmark datasets. |
Read before Generate! Faithful Long Form Question Answering with Machine Reading (2022.findings-acl)
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| Challenge: | Long-form question answering (LFQA) generates a paragraph-length answer for a given question. |
| Approach: | They propose a framework that jointly models answer generation and machine reading. |
| Outcome: | The proposed model generates a more factually accurate answer from millions of documents retrieved from a large dataset. |
UCS: Estimating Unseen Coverage for Improved In-Context Learning (2026.findings-acl)
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| Challenge: | Existing selection methods prioritize heuristic notions of relevance or diversity and provide limited insight into the coverage of a demonstration set. |
| Approach: | They propose a training-free, subset-level coverage prior that is unrevealed by a model-consistent embedding and a Smoothed Good-Turing estimator to estimate the number of unrevelled clusters within a candidate subset. |
| Outcome: | Experiments on multiple intent-classification and reasoning benchmarks show that augmenting strong baselines with UCS improves ICL accuracy by 2-6% under the same selection budget. |
LLM-OREF: An Open Relation Extraction Framework Based on Large Language Models (2025.emnlp-main)
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| Challenge: | Existing studies focus on building models that can only handle predefined relations . however, their reliance on human annotation limits their practicality . |
| Approach: | They propose an open relation extraction framework that can generalize to new relations not encountered during training. |
| Outcome: | The proposed framework can generalize to new relations not encountered during training. |
BAPO: Boundary-Aware Policy Optimization for Reliable Agentic Search (2026.findings-acl)
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Shiyu Liu, Yongjing Yin, Jianhao Yan, Yunbo Tang, Qinggang Zhang, Bei Li, Xin Chen, Jingang Wang, Xunliang Cai, Jinsong Su
| Challenge: | Existing RL-based agentic search models fail to recognize reasoning boundaries and rarely admit "I DON'T KNOW" lack of reliability leads to plausible but unreliable answers, introducing significant risks . |
| Approach: | They propose a framework to cultivate reliable boundary awareness without compromising accuracy. |
| Outcome: | Experiments show that the proposed framework improves the reliability of agentic search models. |
Too Consistent to Detect: A Study of Self-Consistent Errors in LLMs (2025.emnlp-main)
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Hexiang Tan, Fei Sun, Sha Liu, Du Su, Qi Cao, Xin Chen, Jingang Wang, Xunliang Cai, Yuanzhuo Wang, Huawei Shen, Xueqi Cheng
| Challenge: | Existing detection methods fail to account for **self-consistent error** . study identifies self-consistency errors and evaluates them . |
| Approach: | They propose a method that fuses hidden state evidence from an external verifier LLM to detect self-consistent errors. |
| Outcome: | The proposed method significantly enhances performance on self-consistent errors across three LLM families. |
Multimodal Language Models See Better When They Look Shallower (2025.emnlp-main)
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Haoran Chen, Junyan Lin, Xinghao Chen, Yue Fan, Jianfeng Dong, Xin Jin, Hui Su, Jinlan Fu, Xiaoyu Shen
| Challenge: | Existing studies show that multimodal large language models extract visual features from the final layers of a pretrained Vision Transformer. |
| Approach: | They propose a feature fusion method that strategically incorporates shallower layers . they propose MLLMs that extract visual features from the final layers of a pretrained Vision Transformer . |
| Outcome: | The proposed method outperforms deep layers on fine-grained visual tasks . it is the first comprehensive study of visual layer selection for MLLMs . |
Getting the Most out of Simile Recognition (2022.findings-emnlp)
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| Challenge: | Recent work ignores features other than surface strings and suffers from data hunger issue. |
| Approach: | They propose to use simile sentence classification and simile component extraction to find simile components. |
| Outcome: | The proposed model outperforms current state-of-the-art systems and baselines. |
Semi-Structured Chain-of-Thought: Integrating Multiple Sources of Knowledge for Improved Language Model Reasoning (2024.naacl-long)
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| Challenge: | Existing prompting methods rely on only one or two of these sources, or require repeatedly invoking large language models to generate similar or identical content. |
| Approach: | They propose a semi-structured prompting approach that integrates parametric memory with unstructured knowledge from text documents and structured knowledge from knowledge graphs. |
| Outcome: | The proposed prompting method surpasses existing prompting methods even exceeding those that require fine-tuning on open-domain multi-hop question answering datasets. |
Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering (2026.acl-long)
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Shuliang Liu, Songbo Yang, Dong Fang, Sihang Jia, Yuqi Tang, Lingfeng Su, Ruoshui Peng, Yibo Yan, Xin Zou, Xuming Hu
| Challenge: | Existing approaches to overcome object hallucination are limited . Existing mitigations include costly retraining and a training-free inference framework . |
| Approach: | They propose a training-free inference framework that simulates a metacognitive self-correction process. |
| Outcome: | The proposed framework reduces object hallucination rates by 12.67% on MMHal-Bench and improves accuracy by 5.8% on POPE. |
TARN-VIST: Topic Aware Reinforcement Network for Visual Storytelling (2024.lrec-main)
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| Challenge: | Existing methods for visual storytelling ignore latent topic information. |
| Approach: | They propose a topic-aware reinforcement network for VIsual StoryTelling that takes topic information into account to generate a coherent story. |
| Outcome: | The proposed method outperforms most of the competing models across multiple evaluation metrics. |
XL-NBT: A Cross-lingual Neural Belief Tracking Framework (D18-1)
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| Challenge: | a multi-lingual approach to training dialog systems is expensive and tedious, but it can be useful for cross-lingual support. |
| Approach: | They propose to annotate data for multiple languages and train a multi-lingual dialog system for each language. |
| Outcome: | The proposed framework bypasses the expensive human annotation and achieves promising results. |
A Comparison of Strategies for Source-Free Domain Adaptation (2022.acl-long)
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| Challenge: | Existing research on domain adaptation without access to training data is limited due to privacy concerns. |
| Approach: | They compare active learning, self-training, and data augmentation strategies for source-free domain adaptation with a shared task. |
| Outcome: | The proposed algorithms yield consistent gains across all SemEval 2021 Task 10 tasks and domains, but they are unreliable for source-free domain adaptation. |
Transformer-Based Temporal Information Extraction and Application: A Review (2025.emnlp-main)
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| Challenge: | Temporal information extraction (IE) aims to extract structured temporal information from unstructured text, thereby uncovering the implicit timelines within. |
| Approach: | They summarize and analyze the work using Transformers to highlight potential future directions. |
| Outcome: | The proposed method is applied across healthcare, newswire, and intelligence analysis domains. |
DAST: Context-Aware Compression in LLMs via Dynamic Allocation of Soft Tokens (2025.findings-acl)
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Shaoshen Chen, Yangning Li, Zishan Xu, Yongqin Zeng, Shunlong Wu, Xinshuo Hu, Zifei Shan, Xin Su, Jiwei Tang, Yinghui Li, Hai-Tao Zheng
| Challenge: | Existing semantic vector-based compression methods do not account for the intrinsic information density variations between context chunks, instead allocating soft tokens uniformly across context chunk. |
| Approach: | They propose a method that leverages the LLM's intrinsic understanding of contextual relevance to guide compression. |
| Outcome: | The proposed method surpasses state-of-the-art methods on long context tasks. |
Personalized Question Answering with User Profile Generation and Compression (2025.findings-emnlp)
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| Challenge: | Large language models are prone to providing “midguy” answers regardless of users’ knowledge background, thereby failing to meet each user’s personalized needs. |
| Approach: | They propose to generate personalized answers with LLMs based on users’ past question-answering records. |
| Outcome: | The proposed method generates personalized answers based on user's past question-answering records. |
PretrainRL: Alleviating Factuality Hallucination of Large Language Models at the Beginning (2026.findings-acl)
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Langming Liu, Kangtao Lv, Haibin Chen, Weidong Zhang, Yejing Wang, Shilei Liu, Xin Tong, Yujin Yuan, Yongwei Wang, Wenbo Su, Bo Zheng
| Challenge: | Large language models suffer from factual hallucinations where they generate verifiable falsehoods. |
| Approach: | They propose a framework that integrates reinforcement learning into the pretraining phase to consolidate factual knowledge. |
| Outcome: | The proposed framework significantly alleviates factual hallucinations and outperforms state-of-the-art methods. |
Optimizing NLU Reranking Using Entity Resolution Signals in Multi-domain Dialog Systems (2021.naacl-industry)
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Tong Wang, Jiangning Chen, Mohsen Malmir, Shuyan Dong, Xin He, Han Wang, Chengwei Su, Yue Liu, Yang Liu
| Challenge: | In dialog systems, the Natural Language Understanding component makes the interpretation decision before the mentioned entities are resolved. |
| Approach: | They propose to leverage Entity Resolution (ER) features in NLU reranking to learn model weights . they propose a score distribution matching method to ensure the models are calibrated . |
| Outcome: | The proposed approach outperforms the baseline model on multiple domain evaluations. |
BaseCal: Unsupervised Confidence Calibration via Base Model Signals (2026.acl-long)
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Hexiang Tan, Wanli Yang, Junwei Zhang, Xin Chen, Rui Tang, Du Su, Jingang Wang, Yuanzhuo Wang, Fei Sun, Xueqi Cheng
| Challenge: | Post-trained LLMs typically compromise reliability with severe overconfidence, resulting in inaccurate responses. |
| Approach: | They propose a solution that feeds PoLLMs into the base LLM to get confidence. |
| Outcome: | The proposed solution reduces expected calibration error (ECE) by 42.90% compared to the best unsupervised baselines. |
IBADR: an Iterative Bias-Aware Dataset Refinement Framework for Debiasing NLU models (2023.emnlp-main)
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| Challenge: | Using manual data analysis, dataset refinement approaches are often unable to cover all the potential biased features. |
| Approach: | They propose an iterative bias-aware dataset refinement framework which debiases NLU models without predefining biased features. |
| Outcome: | The proposed framework outperforms existing methods and is compatible with model-centric methods. |