Papers by Kaize Ding
AMANDA: Agentic Medical Knowledge Augmentation for Data-Efficient Medical Visual Question Answering (2025.findings-emnlp)
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| Challenge: | Existing Med-MLLMs fail when deployed in low-resource settings where abundant labeled data is unavailable. |
| Approach: | They propose a training-free agentic framework that performs medical knowledge augmentation via LLM agents. |
| Outcome: | The proposed framework performs medical knowledge augmentation via LLM agents. |
Empowering Large Language Models for Textual Data Augmentation (2024.findings-acl)
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| Challenge: | True. True. False |
| Approach: | False slants are proposed to generate a large pool of augmentation instructions and select the most suitable task-informed instructions. |
| Outcome: | False omissions: the proposed approach consistently generates augmented data with better quality compared to non-LLM and LLM-based data augmentation methods. |
ALERT: An LLM-powered Benchmark for Automatic Evaluation of Recommendation Explanations (2025.naacl-long)
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Yichuan Li, Xinyang Zhang, Chenwei Zhang, Mao Li, Tianyi Liu, Pei Chen, Yifan Gao, Kyumin Lee, Kaize Ding, Zhengyang Wang, Zhihan Zhang, Jingbo Shang, Xian Li, Trishul Chilimbi
| Challenge: | Existing benchmarks for recommendation explanation evaluation lack item diversity and user preferences data. |
| Approach: | They propose a model-agnostic recommendation explanation evaluation benchmark based on Amazon e-commerce categories with implicit preferences . they propose two novel automatic evaluators that enable scalable and human-preference aligned evaluation of explanations . |
| Outcome: | The proposed model-agnostic evaluation benchmark outperforms existing methods in a variety of domains. |
On Fake News Detection with LLM Enhanced Semantics Mining (2024.emnlp-main)
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| Challenge: | Existing methods for detecting fake news use only news embeddings to capture the lexical semantics between tokens. |
| Approach: | They propose a topic-based model with prompts to extract news embeddings from LLMs and a generalized page-rank model to extract local and global semantics. |
| Outcome: | The proposed model shows superior performance on five benchmark datasets over seven baseline methods. |
Learning to Selectively Learn for Weakly-supervised Paraphrase Generation (2021.emnlp-main)
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| Challenge: | Existing approaches to generate paraphrases with weak supervision are limited in real-world scenarios due to the lack of coherent and controllable generated paraphrase. |
| Approach: | They propose a method to generate high-quality paraphrases with weak supervision . they obtain abundant weakly-labeled parallel sentences via retrieval-based pseudo paraphrase expansion . |
| Outcome: | The proposed approach achieves significant improvements over existing methods and is even comparable in performance with supervised state-of-the-arts. |
Large Language Models for Anomaly and Out-of-Distribution Detection: A Survey (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) have demonstrated their effectiveness in natural language processing but also in broader applications due to their advanced comprehension and generative capabilities. |
| Approach: | They propose a taxonomy to categorize existing approaches into two classes based on the role played by LLMs. |
| Outcome: | The proposed taxonomy categorizes existing approaches into two classes based on the role played by LLMs. |
Be More with Less: Hypergraph Attention Networks for Inductive Text Classification (2020.emnlp-main)
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| Challenge: | Text classification is a critical research topic with broad applications in natural language processing. graph neural networks (GNNs) have received increasing attention but their performance is jeopardized in practice. |
| Approach: | They propose a model which captures long-distance interactions between words and a graph-based model which can be used to perform text classification. |
| Outcome: | The proposed model can achieve more expressive power with less computational consumption on the text classification task. |
Avoiding Copyright Infringement via Large Language Model Unlearning (2025.findings-naacl)
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| Challenge: | Pre-trained Large Language Models (LLMs) have demonstrated remarkable capabilities but also pose significant legal and ethical concerns. |
| Approach: | They propose a framework that unlearns copyrighted content from large language models over multiple time steps by identifying and removing specific weight updates in the model’s parameters that correspond to copyright content. |
| Outcome: | The proposed framework achieves an effective trade-off between unlearning efficacy and general-purpose language abilities, outperforming baselines. |
AD-LLM: Benchmarking Large Language Models for Anomaly Detection (2025.findings-acl)
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Tiankai Yang, Yi Nian, Li Li, Ruiyao Xu, Yuangang Li, Jiaqi Li, Zhuo Xiao, Xiyang Hu, Ryan A. Rossi, Kaize Ding, Xia Hu, Yue Zhao
| Challenge: | Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. |
| Approach: | They propose a benchmark that evaluates how large language models (LLMs) can help with NLP anomaly detection. |
| Outcome: | The proposed model can perform zero-shot detection without tasks-specific training, data augmentation and model selection, and it can suggest unsupervised AD models. |
Exploring Concept Depth: How Large Language Models Acquire Knowledge and Concept at Different Layers? (2025.coling-main)
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Mingyu Jin, Qinkai Yu, Jingyuan Huang, Qingcheng Zeng, Zhenting Wang, Wenyue Hua, Haiyan Zhao, Kai Mei, Yanda Meng, Kaize Ding, Fan Yang, Mengnan Du, Yongfeng Zhang
| Challenge: | Large language models have shown remarkable performances across a wide range of tasks, but mechanisms by which they encode tasks of varying complexity remain poorly understood. |
| Approach: | They propose to explore the possibility that LLMs process concepts in different layers . they propose to categorize concepts based on their level of abstraction . |
| Outcome: | The proposed model can process complex concepts in shallow layers, the authors show . the proposed model could be used to prob complex tasks in shallow ones . |
A Survey of Large Language Models for Text-Guided Molecular Discovery: From Molecule Generation to Optimization (2026.acl-long)
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| Challenge: | Large language models (LLMs) are introducing a paradigm shift in molecular discovery by enabling text-guided interaction with chemical spaces through natural language and symbolic notations. |
| Approach: | They analyze the current LLM learning paradigms to tackle four critical evaluation dimensions that have emerged as critical dimensions in recent studies. |
| Outcome: | The proposed models are able to interact with chemical spaces through natural language and symbolic notations, and have emerging extensions to incorporate multi-modal inputs. |
GRENADE: Graph-Centric Language Model for Self-Supervised Representation Learning on Text-Attributed Graphs (2023.findings-emnlp)
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| Challenge: | Existing methods for self-supervised representation learning on text-attributed graphs lack the full extent of structural context information or rely on task-specific training labels. |
| Approach: | They propose a Graph-Centric Language model that harnesses the synergy of pre-trained language model and graph neural network to optimize with graph-centric contrastive learning and graph-centered knowledge alignment. |
| Outcome: | The proposed model captures informative textual semantics as well as structural context information on text-attributed graphs. |
Addressing Overthinking in Large Vision-Language Models via Gated Perception-Reasoning Optimization (2026.findings-acl)
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Xingjian Diao, Zheyuan Liu, Chunhui Zhang, Weiyi Wu, Keyi Kong, Lin Shi, Kaize Ding, Soroush Vosoughi, Jiang Gui
| Challenge: | Prior work has attempted to mitigate this issue by using adaptive reasoning strategies, but these methods overlook a fundamental bottleneck: visual perception failures. |
| Approach: | They propose a meta-reasoning controller that dynamically routes computation among three decision paths at each generation step. |
| Outcome: | The proposed method outperforms slow-thinking methods while producing shorter responses. |
Explainable and Fine-Grained Safeguarding of LLM Multi-Agent Systems via Bi-Level Graph Anomaly Detection (2026.acl-long)
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Junjun Pan, Yixin Liu, Rui Miao, Kaize Ding, Yu Zheng, Quoc Viet Hung Nguyen, Alan Wee-Chung Liew, Shirui Pan
| Challenge: | Existing graph anomaly detection methods rely on coarse sentence-level information and overlook fine-grained lexical cues, limiting their reliability and real-world applicability. |
| Approach: | They propose an explainable and fine-grained safeguarding framework for detecting malicious agents in multi-agent systems (MAS) to incorporate both coarse and fine lexical information for anomalous agent identification. |
| Outcome: | Extensive experiments across diverse MAS topologies and attack scenarios demonstrate robust detection performance and strong interpretability of XG-Guard. |
Let’s Ask GNN: Empowering Large Language Model for Graph In-Context Learning (2024.findings-emnlp)
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| Challenge: | Textual Attributed Graphs (TAGs) are crucial for modeling complex real-world systems, yet leveraging large language models (LLMs) for TAGs presents unique challenges due to the gap between sequential text processing and graph-structured data. |
| Approach: | They propose a novel approach that leverages In-Context Learning to integrate graph data and task-specific information into large language models (LLMs) they employ a Graph Neural Network-powered structure-enhanced retriever to select labeled nodes across graphs, incorporating complex graph structures and their supervision signals. |
| Outcome: | Experiments on three tasks and seven LLMs show that AskGNN performs better than existing methods. |
CoAct: Co-Active LLM Preference Learning with Human-AI Synergy (2026.acl-long)
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| Challenge: | Existing methods to learn from preference-based feedback are expensive and scarce. |
| Approach: | They propose a framework that synergistically combines self-rewarding and active learning through human-AI collaboration. |
| Outcome: | The proposed framework outperforms existing methods on three reasoning benchmarks and achieves average improvements of +13.25% on GSM8K, +8.19% on MATH, and +13.16% on WebInstruct. |
MolMem: Memory-Augmented Agentic Reinforcement Learning for Sample-Efficient Molecular Optimization (2026.acl-long)
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| Challenge: | Existing methods for molecular optimization use expensive oracle evaluations to achieve sample efficiency under limited oracular budget. |
| Approach: | They propose a framework that iteratively refines a lead compound to improve molecular properties while preserving structural similarity to the original molecule. |
| Outcome: | The proposed framework achieves 90% success on single-property tasks and 52% on multi-propety task using only 500 oracle calls. |
Explaining Length Bias in LLM-Based Preference Evaluations (2025.findings-emnlp)
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Zhengyu Hu, Linxin Song, Jieyu Zhang, Zheyuan Xiao, Tianfu Wang, Zhengyu Chen, Nicholas Jing Yuan, Jianxun Lian, Kaize Ding, Hui Xiong
| Challenge: | a preference evaluation metric is often biased towards longer responses, revealing a reliability problem . a decomposition of the preference evaluation into two components is needed to understand this bias. |
| Approach: | They propose to decompose the preference evaluation metric into two key components . the first component is length-dependent and related to trustworthiness . |
| Outcome: | The proposed evaluation metric is based on two components: desirability and information mass. |