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Found 20 papers, 13 papers with code
Generating Diverse Criteria On-the-Fly to Improve Point-wise LLM Rankers
The most recent pointwise Large Language Model (LLM) rankers have achieved remarkable ranking results.
RefuteBench: Evaluating Refuting Instruction-Following for Large Language Models
The application scope of large language models (LLMs) is increasingly expanding.
XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners
Active learning (AL), which aims to construct an effective training set by iteratively curating the most formative unlabeled data for annotation, has been widely used in low-resource tasks.
Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information
However, we observe that merely concatenating sentences in a contextual window does not fully utilize contextual information and can sometimes lead to excessive attention on less informative sentences.
An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
Catastrophic forgetting (CF) is a phenomenon that occurs in machine learning when a model forgets previously learned information while acquiring new knowledge.
Efficient Prediction of Peptide Self-assembly through Sequential and Graphical Encoding
In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides.
Mitigating Catastrophic Forgetting in Task-Incremental Continual Learning with Adaptive Classification Criterion
It is seldom considered to adapt the decision boundary for new representations and in this paper we propose a Supervised Contrastive learning framework with adaptive classification criterion for Continual Learning (SCCL), In our method, a contrastive loss is used to directly learn representations for different tasks and a limited number of data samples are saved as the classification criterion.
Investigating Forgetting in Pre-Trained Representations Through Continual Learning
Intuitively, the representation forgetting can influence the general knowledge stored in pre-trained language models (LMs), but the concrete effect is still unclear.
Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias
It has become cognitive inertia to employ cross-entropy loss function in classification related tasks.
Improving (Dis)agreement Detection with Inductive Social Relation Information From Comment-Reply Interactions
(Dis)agreement detection aims to identify the authors' attitudes or positions (\textit{{agree, disagree, neutral}}) towards a specific text.
What Does the Gradient Tell When Attacking the Graph Structure
To address this issue, we propose a novel surrogate model with multi-level propagation that preserves the node dissimilarity information.
Mere Contrastive Learning for Cross-Domain Sentiment Analysis
Cross-domain sentiment analysis aims to predict the sentiment of texts in the target domain using the model trained on the source domain to cope with the scarcity of labeled data.
Exploiting Sentiment and Common Sense for Zero-shot Stance Detection
Meanwhile, ablation studies prove the significance of each module in our model.
Are Gradients on Graph Structure Reliable in Gray-box Attacks?
These errors arise from rough gradient usage due to the discreteness of the graph structure and from the unreliability in the meta-gradient on the graph structure.
Challenges for Open-domain Targeted Sentiment Analysis
Since previous studies on open-domain targeted sentiment analysis are limited in dataset domain variety and sentence level, we propose a novel dataset consisting of 6, 013 human-labeled data to extend the data domains in topics of interest and document level.
Surrogate Representation Learning with Isometric Mapping for Gray-box Graph Adversarial Attacks
Gray-box graph attacks aim at disrupting the performance of the victim model by using inconspicuous attacks with limited knowledge of the victim model.
PDAML: A Pseudo Domain Adaptation Paradigm for Subject-independent EEG-based Emotion Recognition
Usually, the DA methods give relatively promising results than the DG methods but require additional computation resources each time a new subject comes.
Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models
Then, we augment the original training datasets with a different number of generated realistic-like EEG data.
FedVision: An Online Visual Object Detection Platform Powered by Federated Learning
Federated learning (FL) is a promising approach to resolve this challenge.
Real-World Image Datasets for Federated Learning
Federated learning is a new machine learning paradigm which allows data parties to build machine learning models collaboratively while keeping their data secure and private.
