Search Results for author:
Found 93 papers, 40 papers with code
AnswerFact: Fact Checking in Product Question Answering
Product-related question answering platforms nowadays are widely employed in many E-commerce sites, providing a convenient way for potential customers to address their concerns during online shopping.
HiTRANS: A Hierarchical Transformer Network for Nested Named Entity Recognition
Specifically, we first utilize a two-phase module to generate span representations by aggregating context information based on a bottom-up and top-down transformer network.
FACT-AUDIT: An Adaptive Multi-Agent Framework for Dynamic Fact-Checking Evaluation of Large Language Models
Large Language Models (LLMs) have significantly advanced the fact-checking studies.
Unlocking Multimodal Integration in EHRs: A Prompt Learning Framework for Language and Time Series Fusion
Large language models (LLMs) have shown remarkable performance in vision-language tasks, but their application in the medical field remains underexplored, particularly for integrating structured time series data with unstructured clinical notes.
SAIF: A Sparse Autoencoder Framework for Interpreting and Steering Instruction Following of Language Models
We demonstrate how the features we identify can effectively steer model outputs to align with given instructions.
CounterBench: A Benchmark for Counterfactuals Reasoning in Large Language Models
Counterfactual reasoning is widely recognized as one of the most challenging and intricate aspects of causality in artificial intelligence.
LLM-Enhanced Multiple Instance Learning for Joint Rumor and Stance Detection with Social Context Information
Although rumor detection and stance detection are distinct tasks, they can complement each other.
GraphICL: Unlocking Graph Learning Potential in LLMs through Structured Prompt Design
The growing importance of textual and relational systems has driven interest in enhancing large language models (LLMs) for graph-structured data, particularly Text-Attributed Graphs (TAGs), where samples are represented by textual descriptions interconnected by edges.
Backdoor Token Unlearning: Exposing and Defending Backdoors in Pretrained Language Models
The BTU defense leverages these properties to identify aberrant embedding parameters and subsequently removes backdoor behaviors using a fine-grained unlearning technique.
ClarityEthic: Explainable Moral Judgment Utilizing Contrastive Ethical Insights from Large Language Models
This suggests that modeling human moral judgment with the emulating humans moral strategy is promising for improving the ethical behaviors of LLMs.
ScratchEval: Are GPT-4o Smarter than My Child? Evaluating Large Multimodal Models with Visual Programming Challenges
By integrating visual elements and embedded programming logic, ScratchEval requires the model to process both visual information and code structure, thereby comprehensively evaluating its programming intent understanding ability.
VideoAutoArena: An Automated Arena for Evaluating Large Multimodal Models in Video Analysis through User Simulation
To further streamline our evaluation, we introduce VideoAutoBench as an auxiliary benchmark, where human annotators label winners in a subset of VideoAutoArena battles.
Invariant Shape Representation Learning For Image Classification
In this paper, we introduce a novel framework that for the first time develops invariant shape representation learning (ISRL) to further strengthen the robustness of image classifiers.
SHARP: Unlocking Interactive Hallucination via Stance Transfer in Role-Playing Agents
The advanced role-playing capabilities of Large Language Models (LLMs) have paved the way for developing Role-Playing Agents (RPAs).
Multimodal Clinical Reasoning through Knowledge-augmented Rationale Generation
ClinRaGen incorporates a unique knowledge-augmented attention mechanism to merge domain knowledge with time series EHR data, utilizing a stepwise rationale distillation strategy to produce both textual and time series-based clinical rationales.
Towards Low-Resource Harmful Meme Detection with LMM Agents
The proliferation of Internet memes in the age of social media necessitates effective identification of harmful ones.
Global Graph Counterfactual Explanation: A Subgraph Mapping Approach
Methodologically, we design a significant subgraph generator and a counterfactual subgraph autoencoder in our GlobalGCE, where the subgraphs and the rules can be effectively generated.
AMR-Evol: Adaptive Modular Response Evolution Elicits Better Knowledge Distillation for Large Language Models in Code Generation
To this end, our study introduces the Adaptive Modular Response Evolution (AMR-Evol) framework, which employs a two-stage process to refine response distillation.
Causal Inference with Large Language Model: A Survey
Causal inference has been a pivotal challenge across diverse domains such as medicine and economics, demanding a complicated integration of human knowledge, mathematical reasoning, and data mining capabilities.
A Survey of Out-of-distribution Generalization for Graph Machine Learning from a Causal View
Concluding with a discussion on potential future research directions, this review seeks to articulate the continuing development and future potential of causality in enhancing the trustworthiness of graph machine learning.
Certified Causal Defense with Generalizable Robustness
On top of that, we design a causally certified defense strategy to handle adversarial attacks on latent causal factors.
CodeJudge-Eval: Can Large Language Models be Good Judges in Code Understanding?
Recent advancements in large language models (LLMs) have showcased impressive code generation capabilities, primarily evaluated through language-to-code benchmarks.
View-consistent Object Removal in Radiance Fields
In this work, we introduce a novel RF editing pipeline that significantly enhances consistency by requiring the inpainting of only a single reference image.
A Benchmark for Fairness-Aware Graph Learning
Fairness-aware graph learning has gained increasing attention in recent years.
Causal Inference with Latent Variables: Recent Advances and Future Prospectives
Depending on the type of unobserved variables and the specific CI task, various consequences can be incurred if these latent variables are carelessly handled, such as biased estimation of causal effects, incomplete understanding of causal mechanisms, lack of individual-level causal consideration, etc.
MFC-Bench: Benchmarking Multimodal Fact-Checking with Large Vision-Language Models
Large vision-language models (LVLMs) have significantly improved multimodal reasoning tasks, such as visual question answering and image captioning.
Reinforcement Tuning for Detecting Stances and Debunking Rumors Jointly with Large Language Models
Learning multi-task models for jointly detecting stance and verifying rumors poses challenges due to the need for training data of stance at post level and rumor veracity at claim level, which are difficult to obtain.
Cracking the Code of Juxtaposition: Can AI Models Understand the Humorous Contradictions
Through extensive experimentation and analysis of recent commercial or open-sourced large (vision) language models, we assess their capability to comprehend the complex interplay of the narrative humor inherent in these comics.
Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom
To detect fake news from a sea of diverse, crowded and even competing narratives, in this paper, we propose a novel defense-based explainable fake news detection framework.
A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law
In the fast-evolving domain of artificial intelligence, large language models (LLMs) such as GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law: domains characterized by their reliance on professional expertise, challenging data acquisition, high-stakes, and stringent regulatory compliance.
CofiPara: A Coarse-to-fine Paradigm for Multimodal Sarcasm Target Identification with Large Multimodal Models
Current methods for Multimodal Sarcasm Target Identification (MSTI) predominantly focus on superficial indicators in an end-to-end manner, overlooking the nuanced understanding of multimodal sarcasm conveyed through both the text and image.
MMCode: Benchmarking Multimodal Large Language Models for Code Generation with Visually Rich Programming Problems
Programming often involves converting detailed and complex specifications into code, a process during which developers typically utilize visual aids to more effectively convey concepts.
AI WALKUP: A Computer-Vision Approach to Quantifying MDS-UPDRS in Parkinson's Disease
Parkinson's Disease (PD) is the second most common neurodegenerative disorder.
Towards Explainable Harmful Meme Detection through Multimodal Debate between Large Language Models
Then we propose to fine-tune a small language model as the debate judge for harmfulness inference, to facilitate multimodal fusion between the harmfulness rationales and the intrinsic multimodal information within memes.
Ranked #2 on
Hateful Meme Classification
on Harm-P
Cyclic Neural Network
This paper answers a fundamental question in artificial neural network (ANN) design: We do not need to build ANNs layer-by-layer sequentially to guarantee the Directed Acyclic Graph (DAG) property.
GOAT-Bench: Safety Insights to Large Multimodal Models through Meme-Based Social Abuse
The exponential growth of social media has profoundly transformed how information is created, disseminated, and absorbed, exceeding any precedent in the digital age.
Improved Self-Training for Test-Time Adaptation
Test-time adaptation (TTA) is a technique to improve the performance of a pre-trained source model on a target distribution without using any labeled data.
Efficient LLM inference solution on Intel GPU
A customized Scaled-Dot-Product-Attention kernel is designed to match our fusion policy based on the segment KV cache solution.
Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models
The age of social media is rife with memes.
WSDMS: Debunk Fake News via Weakly Supervised Detection of Misinforming Sentences with Contextualized Social Wisdom
This model only requires bag-level labels for training but is capable of inferring both sentence-level misinformation and article-level veracity, aided by relevant social media conversations that are attentively contextualized with news sentences.
Fair Few-shot Learning with Auxiliary Sets
These auxiliary sets contain several labeled training samples that can enhance the model performance regarding fairness in meta-test tasks, thereby allowing for the transfer of learned useful fairness-oriented knowledge to meta-test tasks.
Graph-based Alignment and Uniformity for Recommendation
To address this issue, we propose a novel approach, graph-based alignment and uniformity (GraphAU), that explicitly considers high-order connectivities in the user-item bipartite graph.
A Look into Causal Effects under Entangled Treatment in Graphs: Investigating the Impact of Contact on MRSA Infection
In this paper, we study the problem of causal effect estimation with treatment entangled in a graph.
Learning for Counterfactual Fairness from Observational Data
A prerequisite for existing methods to achieve counterfactual fairness is the prior human knowledge of the causal model for the data.
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Moreover, our model even outperforms the largest closed LLMs, Anthropic's Claude and Google's Bard, on HumanEval and HumanEval+.
Ranked #7 on
Code Generation
on CodeContests
A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises
This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs), including their construction, utilization models, and applications across various healthcare and biomedical research domains.
Path-Specific Counterfactual Fairness for Recommender Systems
But since sensitive features may also affect user interests in a fair manner (e. g., race on culture-based preferences), indiscriminately eliminating all the influences of sensitive features inevitably degenerate the recommendations quality and necessary diversities.
Augmented Large Language Models with Parametric Knowledge Guiding
We demonstrate that our PKG framework can enhance the performance of "black-box" LLMs on a range of domain knowledge-intensive tasks that require factual (+7. 9%), tabular (+11. 9%), medical (+3. 0%), and multimodal (+8. 1%) knowledge.
A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection
The truth is significantly hampered by massive rumors that spread along with breaking news or popular topics.
Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes
Memes have gained popularity as a means to share visual ideas through the Internet and social media by mixing text, images and videos, often for humorous purposes.
NUAA-QMUL-AIIT at Memotion 3: Multi-modal Fusion with Squeeze-and-Excitation for Internet Meme Emotion Analysis
This paper describes the participation of our NUAA-QMUL-AIIT team in the Memotion 3 shared task on meme emotion analysis.
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Retrieval
The conventional dense retrieval paradigm relies on encoding images and texts into dense representations using dual-stream encoders, however, it faces challenges with low retrieval speed in large-scale retrieval scenarios.
Causal Inference in Recommender Systems: A Survey of Strategies for Bias Mitigation, Explanation, and Generalization
Traditional RSs estimate user interests and predict their future behaviors by utilizing correlations in the observational historical activities, their profiles, and the content of interacted items.
LexLIP: Lexicon-Bottlenecked Language-Image Pre-Training for Large-Scale Image-Text Sparse Retrieval
To address this issue, we propose a novel sparse retrieval paradigm for ITR that exploits sparse representations in the vocabulary space for images and texts.
Zero-Shot Rumor Detection with Propagation Structure via Prompt Learning
The spread of rumors along with breaking events seriously hinders the truth in the era of social media.
Interpreting Unfairness in Graph Neural Networks via Training Node Attribution
In this paper, we study a novel problem of interpreting GNN unfairness through attributing it to the influence of training nodes.
Private Semi-supervised Knowledge Transfer for Deep Learning from Noisy Labels
PATE combines an ensemble of "teacher models" trained on sensitive data and transfers the knowledge to a "student" model through the noisy aggregation of teachers' votes for labeling unlabeled public data which the student model will be trained on.
CLEAR: Generative Counterfactual Explanations on Graphs
Counterfactual explanations promote explainability in machine learning models by answering the question "how should an input instance be perturbed to obtain a desired predicted label?".
DPAR: Decoupled Graph Neural Networks with Node-Level Differential Privacy
The key idea is to decouple the feature projection and message passing via a DP PageRank algorithm which learns the structure information and uses the top-$K$ neighbors determined by the PageRank for feature aggregation.
A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection
Existing fake news detection methods aim to classify a piece of news as true or false and provide veracity explanations, achieving remarkable performances.
Ranked #3 on
Fake News Detection
on RAWFC
Learning Causal Effects on Hypergraphs
Hypergraphs provide an effective abstraction for modeling multi-way group interactions among nodes, where each hyperedge can connect any number of nodes.
Aligning Logits Generatively for Principled Black-Box Knowledge Distillation
Black-Box Knowledge Distillation (B2KD) is a formulated problem for cloud-to-edge model compression with invisible data and models hosted on the server.
Empowering Next POI Recommendation with Multi-Relational Modeling
Noticeably, LBSNs have offered unparalleled access to abundant heterogeneous relational information about users and POIs (including user-user social relations, such as families or colleagues; and user-POI visiting relations).
Fairness in Graph Mining: A Survey
Recently, algorithmic fairness has been extensively studied in graph-based applications.
DecBERT: Enhancing the Language Understanding of BERT with Causal Attention Masks
In contrast, Transformer Decoder with the causal attention masks is naturally sensitive to the word order.
Detect Rumors in Microblog Posts for Low-Resource Domains via Adversarial Contrastive Learning
Massive false rumors emerging along with breaking news or trending topics severely hinder the truth.
A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning
The diffusion of rumors on microblogs generally follows a propagation tree structure, that provides valuable clues on how an original message is transmitted and responded by users over time.
I-Tuning: Tuning Frozen Language Models with Image for Lightweight Image Captioning
Different to these heavy-cost models, we introduce a lightweight image captioning framework (I-Tuning), which contains a small number of trainable parameters.
A Frustratingly Simple Approach for End-to-End Image Captioning
Before training the captioning models, an extra object detector is utilized to recognize the objects in the image at first.
Learning Fair Node Representations with Graph Counterfactual Fairness
In this framework, we generate counterfactuals corresponding to perturbations on each node's and their neighbors' sensitive attributes.
Coarse-To-Fine Incremental Few-Shot Learning
Such images form a new training set (i. e., support set) so that the incremental model is hoped to recognize a basenji (i. e., query) as a basenji next time.
Malicious Mode Attack on EV Coordinated Charging Load and MIADRC Defense Strategy
Further, to mitigate the impact of MMA, a defense strategy based on multi-index information active disturbance rejection control is proposed to improve the stability and anti-disturbance ability of the power system, which considers the impact factors of both mode damping and disturbance compensation.
Cross-lingual COVID-19 Fake News Detection
The COVID-19 pandemic poses a great threat to global public health.
Rumor Detection on Twitter with Claim-Guided Hierarchical Graph Attention Networks
Rumors are rampant in the era of social media.
Analyzing the Implicit Position Encoding Ability of Transformer Decoder
A common limitation of Transformer Encoder's self-attention mechanism is that it cannot automatically capture the information of word order, so one needs to feed the explicit position encodings into the target model.
Communication Efficient Generalized Tensor Factorization for Decentralized Healthcare Networks
Tensor factorization has been proved as an efficient unsupervised learning approach for health data analysis, especially for computational phenotyping, where the high-dimensional Electronic Health Records (EHRs) with patients' history of medical procedures, medications, diagnosis, lab tests, etc., are converted to meaningful and interpretable medical concepts.
Temporal Network Embedding via Tensor Factorization
Representation learning on static graph-structured data has shown a significant impact on many real-world applications.
Multi-objective optimization and explanation for stroke risk assessment in Shanxi province
The experimental results showed that the QIDNN model with 7 interactive features achieve the state-of-art accuracy $83. 25\%$.
Federated Graph Classification over Non-IID Graphs
Federated learning has emerged as an important paradigm for training machine learning models in different domains.
Analysis and classification of main risk factors causing stroke in Shanxi Province
Evaluation of the risk of getting stroke is important for the prevention and treatment of stroke in China.
BIG-bench Machine Learning
Interpretable Machine Learning
+1
Assessing the Causal Impact of COVID-19 Related Policies on Outbreak Dynamics: A Case Study in the US
Besides, as the confounders may be time-varying during COVID-19 (e. g., vigilance of residents changes in the course of the pandemic), it is even more difficult to capture them.
Learning from Crowds by Modeling Common Confusions
Crowdsourcing provides a practical way to obtain large amounts of labeled data at a low cost.
Ranked #1 on
Image Classification
on LabelMe
Debunking Rumors on Twitter with Tree Transformer
Rumors are manufactured with no respect for accuracy, but can circulate quickly and widely by {``}word-of-post{''} through social media conversations.
NUAA-QMUL at SemEval-2020 Task 8: Utilizing BERT and DenseNet for Internet Meme Emotion Analysis
This paper describes our contribution to SemEval 2020 Task 8: Memotion Analysis.
Transferable Multi-level Attention Neural Network for Accurate Prediction of Quantum Chemistry Properties via Multi-task Learning
The development of efficient models for predicting specific properties through machine learning is of great importance for the innovation of chemistry and material science.
Ranked #7 on
Formation Energy
on QM9
Spatio-Temporal Tensor Sketching via Adaptive Sampling
In this paper, we propose SkeTenSmooth, a novel tensor factorization framework that uses adaptive sampling to compress the tensor in a temporally streaming fashion and preserves the underlying global structure.
Review-guided Helpful Answer Identification in E-commerce
In this paper, we propose the Review-guided Answer Helpfulness Prediction (RAHP) model that not only considers the interactions between QA pairs but also investigates the opinion coherence between the answer and crowds' opinions reflected in the reviews, which is another important factor to identify helpful answers.
Privacy-Preserving Tensor Factorization for Collaborative Health Data Analysis
We propose DPFact, a privacy-preserving collaborative tensor factorization method for computational phenotyping using EHR.
Sentence-Level Evidence Embedding for Claim Verification with Hierarchical Attention Networks
Claim verification is generally a task of verifying the veracity of a given claim, which is critical to many downstream applications.
Rumor Detection on Twitter with Tree-structured Recursive Neural Networks
Automatic rumor detection is technically very challenging.
EICA Team at SemEval-2017 Task 3: Semantic and Metadata-based Features for Community Question Answering
In the main Subtask C, our primary submission was ranked fourth, with a MAP of 13. 48 and accuracy of 97. 08.
Detect Rumors in Microblog Posts Using Propagation Structure via Kernel Learning
How fake news goes viral via social media?
