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Found 14 papers, 0 papers with code
ELAD: Explanation-Guided Large Language Models Active Distillation
The deployment and application of Large Language Models (LLMs) is hindered by their memory inefficiency, computational demands, and the high costs of API inferences.
Distilling Large Language Models for Text-Attributed Graph Learning
To address the inherent gaps between LLMs (generative models for texts) and graph models (discriminative models for graphs), we propose first to let LLMs teach an interpreter with rich textual rationale and then let a student model mimic the interpreter's reasoning without LLMs' textual rationale.
AgentLens: Visual Analysis for Agent Behaviors in LLM-based Autonomous Systems
Recently, Large Language Model based Autonomous system(LLMAS) has gained great popularity for its potential to simulate complicated behaviors of human societies.
Explaining latent representations of generative models with large multimodal models
Learning interpretable representations of data generative latent factors is an important topic for the development of artificial intelligence.
XAI Benchmark for Visual Explanation
Our proposed benchmarks facilitate a fair evaluation and comparison of visual explanation methods.
Visual Attention-Prompted Prediction and Learning
To tackle these challenges, we propose a novel framework called Visual Attention-Prompted Prediction and Learning, which seamlessly integrates visual attention prompts into the model's decision-making process and adapts to images both with and without attention prompts for prediction.
SurroCBM: Concept Bottleneck Surrogate Models for Generative Post-hoc Explanation
Explainable AI seeks to bring light to the decision-making processes of black-box models.
Controllable Data Generation Via Iterative Data-Property Mutual Mappings
To address these challenges, in this paper, we propose a general framework to enhance VAE-based data generators with property controllability and ensure disentanglement.
XNLI: Explaining and Diagnosing NLI-based Visual Data Analysis
Our research explores how to provide explanations for NLIs to help users locate the problems and further revise the queries.
MAGI: Multi-Annotated Explanation-Guided Learning
This technique aims to improve the predictability of the model by incorporating human understanding of the prediction process into the training phase.
Multi-objective Deep Data Generation with Correlated Property Control
Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design.
Controllable Data Generation by Deep Learning: A Review
This article is a systematic review that explains this promising research area, commonly known as controllable deep data generation.
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization
Anderson mixing has been heuristically applied to reinforcement learning (RL) algorithms for accelerating convergence and improving the sampling efficiency of deep RL.
An explainable two-dimensional single model deep learning approach for Alzheimer's disease diagnosis and brain atrophy localization
In this research, we propose a novel end-to-end deep learning approach for automated diagnosis of AD and localization of important brain regions related to the disease from sMRI data.
