Papers by Chong Yang
We-Math: Does Your Large Multimodal Model Achieve Human-like Mathematical Reasoning? (2025.acl-long)
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Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Chong Sun, Xiaoshuai Song, Jiapeng Wang, Zhuoma GongQue, Shanglin Lei, YiFan Zhang, Zhe Wei, Miaoxuan Zhang, Runfeng Qiao, Xiao Zong, Yida Xu, Peiqing Yang, Zhimin Bao, Muxi Diao, Chen Li, Honggang Zhang
| Challenge: | Existing benchmarks focus more on end-to-end performance, but neglect the underlying principles of knowledge acquisition and generalization. |
| Approach: | They propose a benchmark specifically designed to explore the problem-solving principles by decomposing 6.5K visual math problems into 10.9K step-level questions for evaluation. |
| Outcome: | The proposed benchmark covers 6.5K visual math problems and 10.9K step-level questions spanning 5 layers of knowledge granularity and 67 hierarchical knowledge concepts. |
Are U a Joke Master? Pun Generation via Multi-Stage Curriculum Learning towards a Humor LLM (2024.findings-acl)
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Yang Chen, Chong Yang, Tu Hu, Xinhao Chen, Man Lan, Li Cai, Xinlin Zhuang, Xuan Lin, Xin Lu, Aimin Zhou
| Challenge: | Existing research has demonstrated that the ability of large language models (LLMs) to generate humorous sentences is limited to producing 25 unique jokes. |
| Approach: | They propose a multi-stage curriculum preference learning framework to optimize both pun structure preferences and humor preferences by a Chinese Pun dataset. |
| Outcome: | The proposed method significantly outperforms baseline models on Chinese and English benchmark datasets. |
S+PAGE: A Speaker and Position-Aware Graph Neural Network Model for Emotion Recognition in Conversation (2022.aacl-main)
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| Challenge: | Emotion recognition in conversation (ERC) is a task arousing increasing interest in many fields. |
| Approach: | They propose a novel GNN-based ERC model that captures speaker and position information. |
| Outcome: | The proposed model captures speaker and position-aware conversation structure information. |
Are Emotion and Rhetoric Neurons in LLM? Neuron Recognition and Adaptive Masking for Emotion-Rhetoric Prediction Steering (2026.acl-long)
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| Challenge: | Existing studies on neurons focus on emotion and rhetoric, neglecting their intrinsic connections. |
| Approach: | They propose a framework for fine-grained steering of emotion and rhetoric in large language models . they propose 'neuro-based' masking method that integrates multi-dimensional screening . |
| Outcome: | The proposed method achieves directed induction of non-target sentences and enhancement of emotion tasks via rhetoric neurons. |
MCTS: A Multi-Reference Chinese Text Simplification Dataset (2024.lrec-main)
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Ruining Chong, Luming Lu, Liner Yang, Jinran Nie, Zhenghao Liu, Shuo Wang, Shuhan Zhou, Yaoxin Li, Erhong Yang
| Challenge: | Existing studies on text simplification systems have focused on unsupervised methods due to the limited evaluation data in language and domain. |
| Approach: | They propose a Chinese text simplification dataset that provides a detailed analysis and an annotation process. |
| Outcome: | The proposed dataset evaluates the performance of unsupervised methods and advanced large language models. |
Decoding Matters: Addressing Amplification Bias and Homogeneity Issue in Recommendations for Large Language Models (2024.emnlp-main)
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| Challenge: | Existing approaches to adapt Large Language Models (LLMs) for recommendation encounter significant challenges such as amplification bias and homogeneity. |
| Approach: | They propose a new decoding approach called Debiasing-Diversifying Decoding (D3) that disables length normalization for ghost tokens to alleviate amplification bias and incorporates a text-free assistant model to encourage tokens less frequently generated by LLMs for counteracting recommendation homogeneity. |
| Outcome: | Extensive experiments on real-world datasets demonstrate the proposed approach’s effectiveness in enhancing accuracy and diversity. |
X-Instruction: Aligning Language Model in Low-resource Languages with Self-curated Cross-lingual Instructions (2024.findings-acl)
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| Challenge: | Large language models respond well in high-resource languages but struggle in low-resourced languages. |
| Approach: | They propose a method to construct cross-lingual instruction following samples with instruction in English and response in low-resource languages. |
| Outcome: | The proposed method builds a large-scale cross-lingual instruction tuning dataset on 10 languages. |
Leveraging Prefix Transfer for Multi-Intent Text Revision (2023.acl-short)
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Ruining Chong, Cunliang Kong, Liu Wu, Zhenghao Liu, Ziye Jin, Liner Yang, Yange Fan, Hanghang Fan, Erhong Yang
| Challenge: | Text revision is a necessary process to improve text quality. |
| Approach: | They propose a multi-intent text revision system that can revise texts without explicit intent annotation. |
| Outcome: | The proposed system outperforms baselines on the IteraTeR dataset and significantly improves the SARI score with more than 3% improvement. |
Global Adaptive Momentum Meets Local Personalized Perturbation: Efficient Federated LLM Fine-Tuning with Zeroth-Order Gradients (2026.acl-long)
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| Challenge: | federated fine-tuning of large language models provides privacy-preserving approach to deploying pervasive generative AI services. |
| Approach: | They propose a federated framework for fine-tuning large language models . they propose unified optimization and local personalized perturbation for ZO gradients . |
| Outcome: | The proposed framework outperforms existing methods for integrating ZO gradients in federated learning over diverse heterogeneous data settings. |
Think Better, Not Longer: Token-Level Marginal Utility for Efficient Reasoning in Large Reasoning Models (2026.acl-long)
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| Challenge: | Large reasoning models (LRMs) generate explicit Chain-of-Thought rationales, but often suffer from "overthinking". |
| Approach: | They propose a unified training framework to synthesize concise reasoning chains by identifying tokens that reduce the model’s likelihood of the correct answer. |
| Outcome: | Experiments on deepSeek-R1-Distill-Qwen backbones show that MUTO yields better efficiency-accuracy Pareto frontier. |
From Observation to Understanding: Front-Door Adjustments with Uncertainty Calibration for Enhancing Egocentric Reasoning in LVLMs (2025.findings-acl)
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Shenshen Li, Wenxin Meng, Lei Wang, Hao Yang, Chong Peng, Peng Yan, Fumin Shen, Jingkuan Song, Heng Tao Shen, Xing Xu
| Challenge: | Existing methods that adapt LVLMs to egocentric tasks overlook critical agent-environment interactions, limiting their ability to perform egoic reasoning. |
| Approach: | They propose a zero-shot paradigm to enhance egocentric reasoning by simulating human causal reasoning by formalizing ego-centric reasoning using a structural causal model. |
| Outcome: | The proposed method improves egocentric reasoning abilities on six tasks. |
Fundamental Capabilities of Large Language Models and their Applications in Domain Scenarios: A Survey (2024.acl-long)
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Jiawei Li, Yizhe Yang, Yu Bai, Xiaofeng Zhou, Yinghao Li, Huashan Sun, Yuhang Liu, Xingpeng Si, Yuhao Ye, Yixiao Wu, 林一冠 林一冠, Bin Xu, Ren Bowen, Chong Feng, Yang Gao, Heyan Huang
| Challenge: | Existing benchmark-based evaluations cannot accurately reflect the performance of real-world applications. |
| Approach: | They propose a reliable strategy for domains to choose more robust LLMs for real-world applications. |
| Outcome: | The proposed strategy addresses the challenges faced by domains to choose more robust LLMs for real-world applications. |
Can LLMs be Good Graph Judge for Knowledge Graph Construction? (2025.emnlp-main)
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| Challenge: | Existing methods for converting unstructured text into structured Knowledge Graphs (KGs) have limitations such as large amount of noise, inaccurate knowledge, and hallucination . |
| Approach: | They propose a GraphJudge framework to reduce noise in real-world documents . they propose Graphjudge to fine-tune a LLM as a graph judge to enhance quality . |
| Outcome: | The proposed framework eliminates noise in real-world documents and improves the quality of generated KGs. |
V-Oracle: Making Progressive Reasoning in Deciphering Oracle Bones for You and Me (2025.acl-long)
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Runqi Qiao, Qiuna Tan, Guanting Dong, MinhuiWu MinhuiWu, Jiapeng Wang, YiFan Zhang, Zhuoma GongQue, Chong Sun, Yida Xu, Yadong Xue, Ye Tian, Zhimin Bao, Lan Yang, Chen Li, Honggang Zhang
| Challenge: | Deciphering oracle bone scripts using AI technology is not an overnight task due to the evolution of written language over millennia. |
| Approach: | They propose a framework that utilizes Large Multi-modal Models (LMMs) for interpreting Oracle Bone Script (OBS). |
| Outcome: | The proposed framework provides quantitative analyses and superior deciphering capability. |
Decoding the Multimodal Mind: Generalizable Brain-to-Text Translation via Multimodal Alignment and Adaptive Routing (2026.findings-acl)
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| Challenge: | Current approaches to decoding language from the human brain rely on unimodal representations, neglecting the brain’s inherently multimodal processing. |
| Approach: | They propose a framework that leverages Multimodal Large Language Models to align brain signals with a shared semantic space encompassing text, images, and audio. |
| Outcome: | The proposed framework achieves an 8.48% improvement on the most commonly used benchmark on fMRI datasets with textual, visual, and auditory stimuli. |
FinDABench: Benchmarking Financial Data Analysis Ability of Large Language Models (2025.coling-main)
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Shu Liu, Shangqing Zhao, Chenghao Jia, Xinlin Zhuang, Zhaoguang Long, Jie Zhou, Aimin Zhou, Man Lan, Yang Chong
| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across a wide range of tasks, but their proficiency and reliability in the specialized domain of financial data analysis remain uncertain. |
| Approach: | FinDABench is a benchmark designed to evaluate the financial data analysis capabilities of Large Language Models (LLMs) it comprises 15,200 training instances and 8,900 test instances, all meticulously crafted by human experts. |
| Outcome: | FinDABench measures the financial data analysis capabilities of large language models (LLMs) across three dimensions: 1) Core Ability; 2) Analytical Ability; 3) Technical Ability. |
Multi-level Adaptive Contrastive Learning for Knowledge Internalization in Dialogue Generation (2023.emnlp-main)
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Chenxu Yang, Zheng Lin, Lanrui Wang, Chong Tian, Liang Pang, Jiangnan Li, Qirong Ho, Yanan Cao, Weiping Wang
| Challenge: | Existing knowledge-grounded dialogue generation models struggle with dull and repetitive outputs, a problem commonly termed as text degeneration. |
| Approach: | They propose a framework that allows the model to "cheat" the objective by duplicating knowledge segments in a superficial pattern matching based on overlap. |
| Outcome: | The proposed framework can be applied to a WoW dataset and shows that it works across models and decoding strategies. |
UniCreative: Unifying Long-form Logic and Short-form Sparkle via Reference-Free Reinforcement Learning (2026.findings-acl)
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Xiaolong Wei, Zerun Zhu, Simin Niu, Xingyu Zhang, Peiying Yu, Changxuan Xiao, Yuchen Li, Jicheng Yang, Zhejun Zhao, Chong Meng, Long Xia, Daiting Shi
| Challenge: | Existing alignment paradigms for creative writing use static reward signals and supervised data. |
| Approach: | They propose a constraint-aware reward model that synthesizes query-specific criteria to provide fine-grained preference judgments. |
| Outcome: | The proposed framework aligns models with human preferences across content quality and structural paradigms without supervised fine-tuning and ground-truth references. |
Efficient Universal Goal Hijacking with Semantics-guided Prompt Organization (2025.acl-long)
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| Challenge: | Existing methods for prompt injection have focused on optimizing the suffix, overlooking the role of the prompt. |
| Approach: | They propose a method that incorporates an efficient optimization algorithm and two semantics-guided prompt organization strategies to optimize the suffix sequence for universal goal hijacking. |
| Outcome: | The proposed method can generate a fixed suffix that can concatenate to arbitrary user prompts for universal goal hijacking. |