Papers by Peng Kang
Human-in-the-Loop Synthetic Text Data Inspection with Provenance Tracking (2024.findings-naacl)
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| Challenge: | Data augmentation techniques generate low-quality texts with incorrect labels . a new technique is needed to winnow out texts with inaccurate labels based on provenance inspection . |
| Approach: | They develop a data inspection technique that uses provenance inspection and assistive labeling to winnow out texts with incorrect labels. |
| Outcome: | a new human-in-the-loop data inspection technique can winnow out texts with incorrect labels . the technique can reduce human inspection effort by combining provenance inspection and assistive labeling . |
A Cross-Domain Transferable Neural Coherence Model (P19-1)
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Peng Xu, Hamidreza Saghir, Jin Sung Kang, Teng Long, Avishek Joey Bose, Yanshuai Cao, Jackie Chi Kit Cheung
| Challenge: | Existing coherence models do not generalize to unseen categories of text . previous work advocates for generative models for cross-domain generalization . |
| Approach: | They propose a local discriminative neural model with a smaller negative sampling space that can discriminate against incorrect orderings. |
| Outcome: | The proposed model outperforms state-of-the-art methods on a standard benchmark dataset on the Wall Street Journal corpus and multiple challenging settings on Wikipedia articles. |
RubricBench: Aligning Model-Generated Rubrics with Human Standards (2026.acl-long)
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Junyi Zhou, Qiyuan Zhang, Yufei Wang, Fuyuan Lyu, Yidong Ming, Can Xu, Qingfeng Sun, Kai Zheng, Peng Kang, Xue Liu, Chen Ma
| Challenge: | Existing benchmarks lack discriminative complexity and ground-truth rubric annotations required for rigorous evaluation. |
| Approach: | They propose a curated benchmark with 1,147 pairwise comparisons to assess the reliability of rubric-based evaluation. |
| Outcome: | The proposed benchmarks show that they support diverse domains, exhibit discriminative ability, provide high-quality annotations, and include human-authored rubrics. |
MM-PoisonRAG: Disrupting Multimodal RAG with Local and Global Knowledge Poisoning Attacks (2026.acl-long)
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Hyeonjeong Ha, Qiusi Zhan, Jeonghwan Kim, Dimitrios Bralios, Saikrishna Sanniboina, Nanyun Peng, Kai-Wei Chang, Daniel Kang, Heng Ji
| Challenge: | Existing research exposes multimodal large language models to knowledge poisoning attacks . localized poisoning attack achieves up to 56% success rate even under restricted access . globalized poison attack completely disrupts model generation to 0% accuracy with just one poisoned content. |
| Approach: | They propose a framework to study the vulnerability of multimodal RAG under knowledge poisoning attacks. |
| Outcome: | The proposed framework exploits two new attack strategies on multimodal RAGs under knowledge poisoning. |
If an LLM Were a Character, Would It Know Its Own Story? Evaluating Lifelong Learning in LLMs (2026.acl-long)
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Siqi Fan, Xiusheng Huang, Yiqun Yao, Xuezhi Fang, Kang Liu, Peng Han, Shuo Shang, Aixin Sun, Yequan Wang
| Challenge: | Existing benchmarks for large language models (LLMs) fail to capture these dynamics, focusing on static, open-ended evaluations. |
| Approach: | They propose a benchmark to assess lifelong learning in large language models . they use two episodic datasets rich in narrative structure and character interactions . |
| Outcome: | Experiments on LLMs show that non-parametric methods outperform parametric ones in managing stateful learning. |
BigVideo: A Large-scale Video Subtitle Translation Dataset for Multimodal Machine Translation (2023.findings-acl)
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Liyan Kang, Luyang Huang, Ningxin Peng, Peihao Zhu, Zewei Sun, Shanbo Cheng, Mingxuan Wang, Degen Huang, Jinsong Su
| Challenge: | Existing datasets focus on captions describing images or videos, which are not large and diverse enough. |
| Approach: | They propose a large-scale video subtitle translation dataset to facilitate multi-modality machine translation. |
| Outcome: | The proposed dataset is 10 times larger than the widely used *How2* and *VaTeX* datasets. |
Beyond Ranking: Fine-Grained Diagnostics and Self-Improvement for MLLMs (2026.acl-long)
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| Challenge: | Current paradigms rely on holistic scoring and static leaderboards to disentangle fine-grained competencies. |
| Approach: | They propose a framework to shift the focus from ranking to fine-grained diagnosis. |
| Outcome: | The proposed framework surpasses the strongest baseline by 7.92%. |
LearnDA: Learnable Knowledge-Guided Data Augmentation for Event Causality Identification (2021.acl-long)
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| 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. |
CODIS: Benchmarking Context-dependent Visual Comprehension for Multimodal Large Language Models (2024.acl-long)
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Fuwen Luo, Chi Chen, Zihao Wan, Zhaolu Kang, Qidong Yan, Yingjie Li, Xiaolong Wang, Siyu Wang, Ziyue Wang, Xiaoyue Mi, Peng Li, Ning Ma, Maosong Sun, Yang Liu
| Challenge: | Multimodal large language models have demonstrated promising results in a variety of tasks that combine vision and language. |
| Approach: | They propose a benchmark to assess the ability of models to use contextual information in free-form text to enhance visual comprehension. |
| Outcome: | The proposed model fails to extract and utilize contextual information to improve understanding of images. |
MUCAR: Benchmarking Multilingual Cross-Modal Ambiguity Resolution for Multimodal Large Language Models (2025.emnlp-main)
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Xiaolong Wang, Zhaolu Kang, Wangyuxuan Zhai, Xinyue Lou, Yunghwei Lai, Ziyue Wang, Yawen Wang, Kaiyu Huang, Yile Wang, Peng Li, Yang Liu
| Challenge: | Existing multimodal benchmarks overlook linguistic and visual ambiguities, authors say . ambiguity resolution between modalities is lacking in multimodal large language models . |
| Approach: | They propose a benchmark to evaluate multimodal ambiguity resolution across multilingual and cross-modal scenarios. |
| Outcome: | a new benchmark evaluates multimodal ambiguity resolution across multilingual and cross-modal scenarios . the benchmark shows that MLLMs can resolve ambiguities in image-text alignment . however, existing benchmarks often overlook linguistic and visual ambiguties . |
Knowledge-Enriched Event Causality Identification via Latent Structure Induction Networks (2021.acl-long)
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| 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. |
Enhancing Chinese Offensive Language Detection with Homophonic Perturbation (2025.emnlp-main)
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| Challenge: | Detecting offensive language in Chinese is challenging due to homophonic substitutions used to evade detection. |
| Approach: | They propose to use HED-COLD to build a large-scale homophonic dataset for Chinese offensive language detection and a homophone-aware pretraining strategy to learn phonetics and orthography. |
| Outcome: | The proposed framework achieves state-of-the-art performance on the COLD test set and the toxicity benchmark ToxiCloakCN. |
Improving Event Causality Identification via Self-Supervised Representation Learning on External Causal Statement (2021.findings-acl)
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| 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). |
EMO-RL: Emotion-Rule-Based Reinforcement Learning Enhanced Audio-Language Model for Generalized Speech Emotion Recognition (2025.findings-emnlp)
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| Challenge: | Recent advances in reinforcement learning (RL) have shown promise in improving LALMs’ reasoning abilities, but their performance in affective computing tasks remains suboptimal. |
| Approach: | They propose a framework incorporating reinforcement learning with two key innovations: Emotion Similarity-Weighted Reward (ESWR) and Explicit Structured Reasoning (ESR). |
| Outcome: | The proposed framework improves LALMs' reasoning abilities on MELD and IEMOCAP datasets and shows strong generalization. |