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Found 24 papers, 16 papers with code
AIR: Analytic Imbalance Rectifier for Continual Learning
To solve this problem, we propose an analytic imbalance rectifier algorithm (AIR), a novel online exemplar-free continual learning method with an analytic (i. e., closed-form) solution for data-imbalanced class-incremental learning (CIL) and generalized CIL scenarios in real-world continual learning.
GenderAlign: An Alignment Dataset for Mitigating Gender Bias in Large Language Models
Furthermore, we categorized the gender biases in the "rejected" responses of GenderAlign into 4 principal categories.
PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration
In this paper, we propose PrivacyRestore to protect the privacy of user inputs during LLM inference.
Online Analytic Exemplar-Free Continual Learning with Large Models for Imbalanced Autonomous Driving Task
One of these scenarios can be formulated as an online continual learning (OCL) problem.
Analytic Federated Learning
In this paper, we introduce analytic federated learning (AFL), a new training paradigm that brings analytical (i. e., closed-form) solutions to the federated learning (FL) community.
Federated Domain-Specific Knowledge Transfer on Large Language Models Using Synthetic Data
In this paper, we introduce a Federated Domain-specific Knowledge Transfer (FDKT) framework, which enables domain-specific knowledge transfer from LLMs to SLMs while preserving clients' data privacy.
Eraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge
Jailbreaking attacks can enable Large Language Models (LLMs) to bypass the safeguard and generate harmful content.
DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning
The compensation stream is governed by a Dual-Activation Compensation (DAC) module.
AOCIL: Exemplar-free Analytic Online Class Incremental Learning with Low Time and Resource Consumption
Online Class Incremental Learning (OCIL) aims to train the model in a task-by-task manner, where data arrive in mini-batches at a time while previous data are not accessible.
G-ACIL: Analytic Learning for Exemplar-Free Generalized Class Incremental Learning
The generalized CIL (GCIL) aims to address the CIL problem in a more real-world scenario, where incoming data have mixed data categories and unknown sample size distribution, leading to intensified forgetting.
On the use of Silver Standard Data for Zero-shot Classification Tasks in Information Extraction
Recent zero-shot classification methods converted the task to other NLP tasks (e. g., textual entailment) and used off-the-shelf models of these NLP tasks to directly perform inference on the test data without using a large amount of IE annotation data.
Chimera: A Lossless Decoding Method for Accelerating Large Language Models Inference by Fusing all Tokens
Within this framework, we introduce a lightweight draft model that effectively utilizes previously generated tokens to predict subsequent words.
Zero-shot Explainable Mental Health Analysis on Social Media by Incorporating Mental Scales
Traditional discriminative approaches in mental health analysis are known for their strong capacity but lack interpretability and demand large-scale annotated data.
ConsistentEE: A Consistent and Hardness-Guided Early Exiting Method for Accelerating Language Models Inference
We propose ConsistentEE, an early exiting method that is consistent in training and inference.
From Ultra-Fine to Fine: Fine-tuning Ultra-Fine Entity Typing Models to Fine-grained
We can simply fine-tune the previously trained model with a small number of examples annotated under this schema.
Adaptive Policy with Wait-$k$ Model for Simultaneous Translation
Simultaneous machine translation (SiMT) requires a robust read/write policy in conjunction with a high-quality translation model.
GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task
In this paper, we approach the FSCIL by adopting analytic learning, a technique that converts network training into linear problems.
Learning with Silver Standard Data for Zero-shot Relation Extraction
In this paper, we propose to first detect a small amount of clean data from silver standard data and then use the selected clean data to finetune the pretrained model.
Weakly Supervised Text Classification using Supervision Signals from a Language Model
In this paper, we propose to query a masked language model with cloze style prompts to obtain supervision signals.
Variational Weakly Supervised Sentiment Analysis with Posterior Regularization
In this paper, we propose a posterior regularization framework for the variational approach to the weakly supervised sentiment analysis to better control the posterior distribution of the label assignment.
Fair Representation Learning for Heterogeneous Information Networks
Recently, much attention has been paid to the societal impact of AI, especially concerns regarding its fairness.
Neural Fair Collaborative Filtering
A growing proportion of human interactions are digitized on social media platforms and subjected to algorithmic decision-making, and it has become increasingly important to ensure fair treatment from these algorithms.
A Variational Approach to Unsupervised Sentiment Analysis
Our objective function is to predict an opinion word given a target word while our ultimate goal is to learn a sentiment classifier.
A Variational Approach to Weakly Supervised Document-Level Multi-Aspect Sentiment Classification
These word pairs can be extracted by using dependency parsers and simple rules.
