Papers by Chen Guang
Generate-on-Graph: Treat LLM as both Agent and KG for Incomplete Knowledge Graph Question Answering (2024.emnlp-main)
Copied to clipboard
Yao Xu, Shizhu He, Jiabei Chen, Zihao Wang, Yangqiu Song, Hanghang Tong, Guang Liu, Jun Zhao, Kang Liu
| Challenge: | Existing methods to integrate LLMs with Knowledge Graphs (KGs) however, these methods are often incomplete to cover all the knowledge required to answer questions. |
| Approach: | They propose to integrate LLMs with Knowledge Graphs (KGs) to address insufficient knowledge and hallucination issues in Large Language Models. |
| Outcome: | The proposed method outperforms existing methods on two datasets. |
CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models (2024.acl-long)
Copied to clipboard
| Challenge: | Current methods for multimodal sarcasm target identification focus on superficial indicators in an end-to-end manner, overlooking the nuanced understanding of multimodal content. |
| Approach: | They propose a multimodal sarcasm target identification framework with a coarse-to-fine paradigm by augmenting sarcasm explainability with reasoning and pre-training knowledge. |
| Outcome: | The proposed framework outperforms state-of-the-art methods and exhibits explainability in deciphering sarcasm as well. |
MinerU2.5: A Decoupled Vision-Language Model for Efficient High-Resolution Document Parsing (2026.acl-industry)
Copied to clipboard
Junbo Niu, Zheng Liu, Zhuangcheng Gu, Bin Wang, Linke Ouyang, Zhiyuan Zhao, Tao Chu, Tianyao He, Fan Wu, Qintong Zhang, Zhenjiang Jin, Guang Liang, Rui Zhang, Wenzheng Zhang, Yuan Qu, Zhifei Ren, Yuefeng Sun, Zirui Tang, Boyu Niu, Yuanhong Zheng, Dongsheng Ma, Ziyang Miao, Hejun Dong, Siyi Qian, Junyuan Zhang, Fangdong Wang, Jingzhou Chen, Xiaomeng Zhao, Liqun Wei, Wei Li, Shasha Wang, RuiLiang Xu, Yuanyuan Cao, Lu Chen, Qianqian Wu, Huaiyu Gu, Lindong Lu, Dechen Lin, null Shenguanlin, Xuanhe Zhou, Linfeng Zhang, Yuhang Zang, Xiaoyi Dong, Jiaqi Wang, Bo Zhang, Lei Bai, Pei Chu, Weijia Li, Jiang Wu, Lijun Wu, Zhenxiang Li, Guangyu Wang, Zhongying Tu, Chao Xu, Kai Chen, Bowen Zhou, Dahua Lin, Wentao Zhang, Conghui He
| Challenge: | Document images are characterized by higher resolutions, denser content, and more complex structural layouts. |
| Approach: | They propose a 1.2B-parameter document parsing vision-language model that decouples layout analysis from local content recognition. |
| Outcome: | The proposed model surpasses general-purpose and domain-specific models on multiple benchmarks while maintaining significantly lower computational overhead. |
AltCLIP: Altering the Language Encoder in CLIP for Extended Language Capabilities (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to build a strong multilingual multimodal representation model are lacking in good-quality text-image pairs. |
| Approach: | They propose a method to build a strong multilingual multimodal representation model using English text-image pairs instead of a model from scratch. |
| Outcome: | The proposed model outperforms the original CLIP model on multilingual multimodal benchmarks. |
PRISM: Probing Reasoning, Instruction, and Source Memory in LLM Hallucinations (2026.acl-long)
Copied to clipboard
Yuhe Wu, Guangyu Wang, Yuran Chen, Jiatong Zhang, Yutong Zhang, Yujie Chen, Jiaming Shang, Guang Zhang, Zhuang Liu
| Challenge: | Existing benchmarks for hallucination evaluation rely on mixed queries and posterior evaluation, which quantifies hallucinosity severity but offers limited insight into where and why they occur. |
| Approach: | They propose a controlled benchmark that disentangles hallucinations into four dimensions: knowledge missing, knowledge errors, reasoning errors, and instruction-following errors. |
| Outcome: | The proposed model disentangles hallucinations into four dimensions: knowledge missing, knowledge errors, reasoning errors, and instruction-following errors. |
Beyond Sequences: Two-dimensional Representation and Dependency Encoding for Code Generation (2025.acl-long)
Copied to clipboard
| Challenge: | Existing code generation approaches represent code as a linear sequence of tokens, but positional encodings compromise generalization . explicit positional encoders sacrifice permutation invariance, imposes a strict order on the input sequence . |
| Approach: | They propose to represent code snippets as two-dimensional entities with explicit encodings . they propose to use dictionary learning to perform semantic matching between code lines . |
| Outcome: | The proposed model captures the hierarchical and spatial structure of code, especially the dependencies between code lines. |
AdamMeme: Adaptively Probe the Reasoning Capacity of Multimodal Large Language Models on Harmfulness (2025.acl-long)
Copied to clipboard
| Challenge: | Existing models that assess mLLMs on harmful meme understanding are inaccurate and lack accuracy. |
| Approach: | They propose a framework that adaptively probes the reasoning capabilities of mLLMs . their framework systematically reveals the varying performance of different target mllms a . |
| Outcome: | The proposed framework systematically reveals the performance of different target mLLMs. |
AgentThink: A Unified Framework for Tool-Augmented Chain-of-Thought Reasoning in Vision-Language Models for Autonomous Driving (2025.findings-emnlp)
Copied to clipboard
Kangan Qian, Sicong Jiang, Yang Zhong, Ziang Luo, Zilin Huang, Tianze Zhu, Kun Jiang, Mengmeng Yang, Zheng Fu, Jinyu Miao, Yining Shi, He Zhe Lim, Li Liu, Tianbao Zhou, Hongyi Wang, Huang Yu, Yifei Hu, Guang Li, Guang Chen, Hao Ye, Lijun Sun, Diange Yang
| Challenge: | Vision-Language Models struggle with hallucinations, inefficient reasoning, and limited real-world validation hinders accurate perception and robust step-by-step reasoning. |
| Approach: | AgentThink integrates Chain-of-Thought reasoning with dynamic, agent-style tool invocation for autonomous driving tasks. |
| Outcome: | Experiments on the DriveLMM-o1 benchmark show AgentThink significantly boosts overall reasoning scores by 53.91% and enhances answer accuracy by 33.54% . |
LLaSA: Large Language and Structured Data Assistant (2025.naacl-long)
Copied to clipboard
Yao Xu, Shizhu He, Jiabei Chen, ZengXiangrong ZengXiangrong, Bingning Wang, Guang Liu, Jun Zhao, Kang Liu
| Challenge: | Structured knowledge grounding (SKG) tasks are a key part of many NLP applications. |
| Approach: | They propose a framework for enhancing LLMs' ability to handle structured data . they represent various types of structured data in a unified hypergraph format . |
| Outcome: | The proposed framework outperforms existing methods on SKG tasks using LoRA finetuning. |
Syntax-Aware Retrieval Augmented Code Generation (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Neural code generation models with token-level retrieval capabilities are often noisy and time-consuming. |
| Approach: | They propose a token-level retrieval augmented code generation method that leverages syntax constraints for the retrieval of datastores. |
| Outcome: | The proposed method reduces the impact of retrieve noise on code generation on two datasets. |
Rethinking Data Mixing from the Perspective of Large Language Models (2026.acl-short)
Copied to clipboard
Yuanjian Xu, Tianze Sun, Changwei Xu, XinLong Zhao, Jianing Hao, Ran Chen, Yang Liu, Ruijie Xu, Stephen Chen, Guang Zhang
| Challenge: | Existing methods to mix data with LLMs have relied on domain definitions derived from intuition. |
| Approach: | They propose a reweighting framework that restructures data scheduling as a graph-constrained optimization problem. |
| Outcome: | The proposed framework achieves competitive performance on GPT-2 models. |
Towards Low-Resource Harmful Meme Detection with LMM Agents (2024.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for harmful meme detection are limited due to the dynamic nature of memes . eliciting knowledge-revising behavior within the LMM agent is a key factor in achieving this goal . |
| Approach: | They propose an agency-driven framework for low-resource harmful meme detection . they use annotated memes to leverage label information as auxiliary signals for model . |
| Outcome: | The proposed framework achieves superior performance than state-of-the-art methods on the low-resource harmful meme detection task. |
MDTeamGPT: Mitigating Context Collapse and Enabling Self-Evolution in Medical Multi-Agent Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Long, multi-round, multirole interaction trajectories lead to severe information dilution and context window overload, triggering context collapse which destabilizes reasoning. |
| Approach: | They propose a multi-agent framework that compresses and reorganizes multi-round consensus. |
| Outcome: | The proposed framework outperforms baselines across text-based and multimodal tasks while demonstrating superior diagnostic performance and stability in complex clinical scenarios. |
FinRipple: Aligning Large Language Models with Financial Market for Event Ripple Effect Awareness (2025.findings-acl)
Copied to clipboard
| Challenge: | Financial markets exhibit complex dynamics where localized events trigger ripple effects across entities. |
| Approach: | They propose a framework that empowers large language models to analyze ripple effects . they use financial theory-guided large-scale reinforcement learning to align LLMs with the market . |
| Outcome: | The proposed framework allows LLMs to analyze ripple effects through financial theory-guided large-scale reinforcement learning. |
Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing methods for rumor detection are limited to the strict relation of user responses or oversimplify the conversation structure. |
| Approach: | They propose a method that reinforces interaction of user opinions while reducing negative impact imposed by irrelevant posts. |
| Outcome: | The proposed method improves performance on three Twitter datasets and can detect rumors at early stages. |
Slot-Gated Modeling for Joint Slot Filling and Intent Prediction (N18-2)
Copied to clipboard
| Challenge: | Existing approaches for slot filling and intent detection have independent attention weights, but they suffer from error propagation due to their independent models. |
| Approach: | They propose a slot gate that focuses on learning the relationship between intent and slot attention vectors to obtain better semantic frame results by the global optimization. |
| Outcome: | The proposed model significantly improves sentence-level semantic frame accuracy with 4.2% and 1.9% relative improvement compared to the attentional model on benchmark ATIS and Snips datasets respectively. |
Crafting Customisable Characters with LLMs: A Persona-Driven Role-Playing Agent Framework (2025.findings-emnlp)
Copied to clipboard
Bohao Yang, Dong Liu, Chenghao Xiao, Kun Zhao, Chen Tang, Chao Li, Lin Yuan, Yang Guang, Chenghua Lin
| Challenge: | Large Language Models (LLMs) are capable of generating human-like text, but the potential for freely customisable characters remains underexplored. |
| Approach: | They propose a framework which employs Large Language Models to create freely customisable characters through personalised characteristic feature injection. |
| Outcome: | The proposed framework provides valuable insights for developing more accurate and customisable human simulacra. |
SLIM: Subtrajectory-Level Elimination for More Effective Reasoning (2025.findings-emnlp)
Copied to clipboard
Xifeng Yao, Chengyuan Ma, Dongyu Lang, Yinhao Ni, Zhiwei Xu, Huarui Xie, Zihao Chen, Guang Shen, Dandan Tu, Yi Bai, Changzheng Zhang
| Challenge: | Notable examples include OpenAI’s o1/o3/o4 series and DeepSeek-R1 . |
| Approach: | They develop a framework to identify suboptimal subtrajectories based on human-established criteria . they also use a sampling algorithm to select data whose reasoning process is free from suboptimally subtravertories to the highest degree . |
| Outcome: | The proposed method reduces the number of suboptimal subtrajectories by 25.9% during the inference process. |
Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning (2022.findings-naacl)
Copied to clipboard
| Challenge: | Existing rumor detection methods are poor at detecting false rumors about breaking news or trending topics due to the lack of training data and prior knowledge. |
| Approach: | They propose an adversarial contrastive learning framework to detect false rumors by adapting features learned from well-resourced rumor data to that of the low-resource. |
| Outcome: | The proposed framework improves on two low-resource datasets and shows superior performance . it overcomes restriction of domain and/or language usage and improves robustness . |
Search-in-Context: Efficient Multi-Hop QA over Long Contexts via Monte Carlo Tree Search with Dynamic KV Retrieval (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to multihop question answering (MHQA) over long contexts are often neglecting explicit reasoning or incurring expensive computational costs due to full-attention mechanisms over long contextuals. |
| Approach: | They propose a framework that integrates Monte Carlo Tree Search (MCTS) with dynamic key-value retrieval to enable iterative, context-aware reasoning. |
| Outcome: | The proposed framework integrates Monte Carlo Tree Search (MCTS) with dynamic key-value (KV) retrieval to enable iterative, context-aware reasoning. |