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Found 36 papers, 12 papers with code
MathVC: An LLM-Simulated Multi-Character Virtual Classroom for Mathematics Education
Mathematical modeling (MM) is considered a fundamental skill for students in STEM disciplines.
Prelimit Coupling and Steady-State Convergence of Constant-stepsize Nonsmooth Contractive SA
Motivated by Q-learning, we study nonsmooth contractive stochastic approximation (SA) with constant stepsize.
Against The Achilles' Heel: A Survey on Red Teaming for Generative Models
Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safety issues as various vulnerabilities are exposed.
Bias Mitigation in Fine-tuning Pre-trained Models for Enhanced Fairness and Efficiency
Fine-tuning pre-trained models is a widely employed technique in numerous real-world applications.
Confidence Matters: Revisiting Intrinsic Self-Correction Capabilities of Large Language Models
We have experimentally observed that LLMs possess the capability to understand the "confidence" in their own responses.
Learning From Failure: Integrating Negative Examples when Fine-tuning Large Language Models as Agents
However, LLMs are optimized for language generation instead of tool use during training or alignment, limiting their effectiveness as agents.
Deep Equilibrium Models are Almost Equivalent to Not-so-deep Explicit Models for High-dimensional Gaussian Mixtures
Deep equilibrium models (DEQs), as a typical implicit neural network, have demonstrated remarkable success on various tasks.
Constant Stepsize Q-learning: Distributional Convergence, Bias and Extrapolation
By connecting the constant stepsize Q-learning to a time-homogeneous Markov chain, we show the distributional convergence of the iterates in Wasserstein distance and establish its exponential convergence rate.
TrustLLM: Trustworthiness in Large Language Models
This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions.
The Good, The Bad, and Why: Unveiling Emotions in Generative AI
Through extensive experiments involving language and multi-modal models on semantic understanding, logical reasoning, and generation tasks, we demonstrate that both textual and visual EmotionPrompt can boost the performance of AI models while EmotionAttack can hinder it.
Mitigating Label Bias in Machine Learning: Fairness through Confident Learning
In this paper, we demonstrate that despite only having access to the biased labels, it is possible to eliminate bias by filtering the fairest instances within the framework of confident learning.
Leveraging Laryngograph Data for Robust Voicing Detection in Speech
Accurately detecting voiced intervals in speech signals is a critical step in pitch tracking and has numerous applications.
Out-of-Distribution Knowledge Distillation via Confidence Amendment
This paper introduces OOD knowledge distillation, a pioneering learning framework applicable whether or not training ID data is available, given a standard network.
CompeteAI: Understanding the Competition Behaviors in Large Language Model-based Agents
Large language models (LLMs) have been widely used as agents to complete different tasks, such as personal assistance or event planning.
Can Large Language Model Comprehend Ancient Chinese? A Preliminary Test on ACLUE
To bridge this gap, we present ACLUE, an evaluation benchmark designed to assess the capability of language models in comprehending ancient Chinese.
MetaAgents: Simulating Interactions of Human Behaviors for LLM-based Task-oriented Coordination via Collaborative Generative Agents
We propose a novel framework that equips collaborative generative agents with human-like reasoning abilities and specialized skills.
Neural Network Augmented Kalman Filter for Robust Acoustic Howling Suppression
Acoustic howling suppression (AHS) is a critical challenge in audio communication systems.
Advancing Acoustic Howling Suppression through Recursive Training of Neural Networks
In this paper, we introduce a novel training framework designed to comprehensively address the acoustic howling issue by examining its fundamental formation process.
Unifying Robustness and Fidelity: A Comprehensive Study of Pretrained Generative Methods for Speech Enhancement in Adverse Conditions
Enhancing speech signal quality in adverse acoustic environments is a persistent challenge in speech processing.
Large Language Models Understand and Can be Enhanced by Emotional Stimuli
In addition to those deterministic tasks that can be automatically evaluated using existing metrics, we conducted a human study with 106 participants to assess the quality of generative tasks using both vanilla and emotional prompts.
CMMLU: Measuring massive multitask language understanding in Chinese
As the capabilities of large language models (LLMs) continue to advance, evaluating their performance becomes increasingly crucial and challenging.
Hierarchical Optimization-Derived Learning
In recent years, by utilizing optimization techniques to formulate the propagation of deep model, a variety of so-called Optimization-Derived Learning (ODL) approaches have been proposed to address diverse learning and vision tasks.
NeuralKalman: A Learnable Kalman Filter for Acoustic Echo Cancellation
The robustness of the Kalman filter to double talk and its rapid convergence make it a popular approach for addressing acoustic echo cancellation (AEC) challenges.
De-biased Representation Learning for Fairness with Unreliable Labels
In other words, the fair pre-processing methods ignore the discrimination encoded in the labels either during the learning procedure or the evaluation stage.
Optimization-Derived Learning with Essential Convergence Analysis of Training and Hyper-training
Recently, Optimization-Derived Learning (ODL) has attracted attention from learning and vision areas, which designs learning models from the perspective of optimization.
Continuous Speech Separation with Recurrent Selective Attention Network
In this paper, we propose to apply recurrent selective attention network (RSAN) to CSS, which generates a variable number of output channels based on active speaker counting.
Value-Function-based Sequential Minimization for Bi-level Optimization
We also extend BVFSM to address BLO with additional functional constraints.
Bias-Tolerant Fair Classification
Therefore, we propose a Bias-TolerantFAirRegularizedLoss (B-FARL), which tries to regain the benefits using data affected by label bias and selection bias.
Nonlinear Hawkes Processes in Time-Varying System
Hawkes processes are a class of point processes that have the ability to model the self- and mutual-exciting phenomena.
Efficient Inference of Flexible Interaction in Spiking-neuron Networks
Hawkes process provides an effective statistical framework for analyzing the time-dependent interaction of neuronal spiking activities.
Digital Collaborator: Augmenting Task Abstraction in Visualization Design with Artificial Intelligence
In the task abstraction phase of the visualization design process, including in "design studies", a practitioner maps the observed domain goals to generalizable abstract tasks using visualization theory in order to better understand and address the users needs.
Fine-grained Image-to-Image Transformation towards Visual Recognition
In this paper, we aim at transforming an image with a fine-grained category to synthesize new images that preserve the identity of the input image, which can thereby benefit the subsequent fine-grained image recognition and few-shot learning tasks.
Fashion Editing with Adversarial Parsing Learning
Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value.
Human-Centered Emotion Recognition in Animated GIFs
The framework consists of a facial attention module and a hierarchical segment temporal module.
End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perception
In this work, we propose a multi-task learning framework to predict the steering angle and speed control simultaneously in an end-to-end manner.
Boundary-based Image Forgery Detection by Fast Shallow CNN
In this paper, we substantiate that Fast SCNN can detect drastic change of chroma and saturation.
