Search Results for author: Zhen Tan

Found 24 papers, 9 papers with code

Large Language Models for Data Annotation: A Survey

1 code implementation21 Feb 2024 Zhen Tan, Alimohammad Beigi, Song Wang, Ruocheng Guo, Amrita Bhattacharjee, Bohan Jiang, Mansooreh Karami, Jundong Li, Lu Cheng, Huan Liu

Furthermore, the paper includes an in-depth taxonomy of methodologies employing LLMs for data annotation, a comprehensive review of learning strategies for models incorporating LLM-generated annotations, and a detailed discussion on primary challenges and limitations associated with using LLMs for data annotation.

Graph Few-shot Class-incremental Learning

1 code implementation23 Dec 2021 Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu

The ability to incrementally learn new classes is vital to all real-world artificial intelligence systems.

Few-Shot Class-Incremental Learning Incremental Learning +2

Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification

1 code implementation11 Dec 2022 Zhen Tan, Song Wang, Kaize Ding, Jundong Li, Huan Liu

More recently, inspired by the development of graph self-supervised learning, transferring pretrained node embeddings for few-shot node classification could be a promising alternative to meta-learning but remains unexposed.

Classification Contrastive Learning +4

Contrastive Meta-Learning for Few-shot Node Classification

1 code implementation27 Jun 2023 Song Wang, Zhen Tan, Huan Liu, Jundong Li

First, we propose to enhance the intra-class generalizability by involving a contrastive two-step optimization in each episode to explicitly align node embeddings in the same classes.

Classification Graph Mining +2

The Wolf Within: Covert Injection of Malice into MLLM Societies via an MLLM Operative

1 code implementation20 Feb 2024 Zhen Tan, Chengshuai Zhao, Raha Moraffah, YiFan Li, Yu Kong, Tianlong Chen, Huan Liu

Unlike direct harmful output generation for MLLMs, our research demonstrates how a single MLLM agent can be subtly influenced to generate prompts that, in turn, induce other MLLM agents in the society to output malicious content.

Misinformation

Degree-Aware Alignment for Entities in Tail

1 code implementation25 May 2020 Weixin Zeng, Xiang Zhao, Wei Wang, Jiuyang Tang, Zhen Tan

Entity alignment (EA) is to discover equivalent entities in knowledge graphs (KGs), which bridges heterogeneous sources of information and facilitates the integration of knowledge.

Entity Alignment Knowledge Graphs +1

Sparsity-Guided Holistic Explanation for LLMs with Interpretable Inference-Time Intervention

2 code implementations22 Dec 2023 Zhen Tan, Tianlong Chen, Zhenyu Zhang, Huan Liu

Large Language Models (LLMs) have achieved unprecedented breakthroughs in various natural language processing domains.

Interpreting Pretrained Language Models via Concept Bottlenecks

1 code implementation8 Nov 2023 Zhen Tan, Lu Cheng, Song Wang, Yuan Bo, Jundong Li, Huan Liu

Pretrained language models (PLMs) have made significant strides in various natural language processing tasks.

Jointly Extracting Multiple Triplets with Multilayer Translation Constraints

no code implementations AAAI-2019 2019 Zhen Tan, Xiang Zhao, Wei Wang, Weidong Xiao

Triplets extraction is an essential and pivotal step in automatic knowledge base construction, which captures structural information from unstructured text corpus.

named-entity-recognition Named Entity Recognition +4

CLEEK: A Chinese Long-text Corpus for Entity Linking

no code implementations LREC 2020 Weixin Zeng, Xiang Zhao, Jiuyang Tang, Zhen Tan, Xuqian Huang

Moreover, we devise a measure to evaluate the difficulty of documents with respect to entity linking, which is then used to characterize the corpus.

Entity Linking

Joint Event Extraction with Hierarchical Policy Network

no code implementations COLING 2020 Peixin Huang, Xiang Zhao, Ryuichi Takanobu, Zhen Tan, Weidong Xiao

Most existing work on event extraction (EE) either follows a pipelined manner or uses a joint structure but is pipelined in essence.

Event Detection Event Extraction

Relation-aware Bidirectional Path Reasoning for Commonsense Question Answering

no code implementations CoNLL (EMNLP) 2021 Junxing Wang, Xinyi Li, Zhen Tan, Xiang Zhao, Weidong Xiao

A bidirectional attention mechanism is applied between the question sequence and the paths that connect entities, which provides us with transparent interpretability.

Knowledge Graphs Question Answering +1

Supervised Graph Contrastive Learning for Few-shot Node Classification

no code implementations29 Mar 2022 Zhen Tan, Kaize Ding, Ruocheng Guo, Huan Liu

Graphs are present in many real-world applications, such as financial fraud detection, commercial recommendation, and social network analysis.

Classification Contrastive Learning +4

Virtual Node Tuning for Few-shot Node Classification

no code implementations9 Jun 2023 Zhen Tan, Ruocheng Guo, Kaize Ding, Huan Liu

Our approach utilizes a pretrained graph transformer as the encoder and injects virtual nodes as soft prompts in the embedding space, which can be optimized with few-shot labels in novel classes to modulate node embeddings for each specific FSNC task.

Classification Graph Representation Learning +2

Inductive Linear Probing for Few-shot Node Classification

no code implementations14 Jun 2023 Hirthik Mathavan, Zhen Tan, Nivedh Mudiam, Huan Liu

Meta-learning has emerged as a powerful training strategy for few-shot node classification, demonstrating its effectiveness in the transductive setting.

Classification Few-Shot Learning +1

Disinformation Detection: An Evolving Challenge in the Age of LLMs

no code implementations25 Sep 2023 Bohan Jiang, Zhen Tan, Ayushi Nirmal, Huan Liu

A holistic exploration for the formation and detection of disinformation is conducted to foster this line of research.

Noise-Robust Fine-Tuning of Pretrained Language Models via External Guidance

no code implementations2 Nov 2023 Song Wang, Zhen Tan, Ruocheng Guo, Jundong Li

Adopting a two-stage paradigm of pretraining followed by fine-tuning, Pretrained Language Models (PLMs) have achieved substantial advancements in the field of natural language processing.

CSGNN: Conquering Noisy Node labels via Dynamic Class-wise Selection

no code implementations20 Nov 2023 YiFan Li, Zhen Tan, Kai Shu, Zongsheng Cao, Yu Kong, Huan Liu

Graph Neural Networks (GNNs) have emerged as a powerful tool for representation learning on graphs, but they often suffer from overfitting and label noise issues, especially when the data is scarce or imbalanced.

Memorization Representation Learning

Contextualization Distillation from Large Language Model for Knowledge Graph Completion

no code implementations28 Jan 2024 Dawei Li, Zhen Tan, Tianlong Chen, Huan Liu

While textual information significantly enhances the performance of pre-trained language models (PLMs) in knowledge graph completion (KGC), the static and noisy nature of existing corpora collected from Wikipedia articles or synsets definitions often limits the potential of PLM-based KGC models.

Knowledge Graph Completion Language Modelling +1

GraphRCG: Self-conditioned Graph Generation via Bootstrapped Representations

no code implementations2 Mar 2024 Song Wang, Zhen Tan, Xinyu Zhao, Tianlong Chen, Huan Liu, Jundong Li

In contrast, in this work, we propose a novel self-conditioned graph generation framework designed to explicitly model graph distributions and employ these distributions to guide the generation process.

Graph Generation

Media Bias Matters: Understanding the Impact of Politically Biased News on Vaccine Attitudes in Social Media

no code implementations6 Mar 2024 Bohan Jiang, Lu Cheng, Zhen Tan, Ruocheng Guo, Huan Liu

News media has been utilized as a political tool to stray from facts, presenting biased claims without evidence.

Causal Inference

Tuning-Free Accountable Intervention for LLM Deployment -- A Metacognitive Approach

no code implementations8 Mar 2024 Zhen Tan, Jie Peng, Tianlong Chen, Huan Liu

Large Language Models (LLMs) have catalyzed transformative advances across a spectrum of natural language processing tasks through few-shot or zero-shot prompting, bypassing the need for parameter tuning.

Decision Making Hallucination

Thought Graph: Generating Thought Process for Biological Reasoning

no code implementations11 Mar 2024 Chi-Yang Hsu, Kyle Cox, Jiawei Xu, Zhen Tan, Tianhua Zhai, Mengzhou Hu, Dexter Pratt, Tianlong Chen, Ziniu Hu, Ying Ding

We present the Thought Graph as a novel framework to support complex reasoning and use gene set analysis as an example to uncover semantic relationships between biological processes.

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