Papers by Kui Jia

3 papers
PAI-Diffusion: Constructing and Serving a Family of Open Chinese Diffusion Models for Text-to-image Synthesis on the Cloud (2024.acl-demos)

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Challenge: Existing diffusion models fail to address the challenges of generating high-quality images from textual descriptions due to its large vocabulary size and complex character relationships.
Approach: They propose a framework that integrates Chinese diffusion models with Alibaba Cloud's Platform for AI and enables the generation of contextually relevant images.
Outcome: The proposed framework integrates with Alibaba Cloud’s Platform for AI, providing accessible and scalable solutions.
Towards Identification and Intervention of Safety-Critical Parameters in Large Language Models (2026.findings-acl)

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Challenge: Existing safety-related methodologies for large language models are lacking . despite advances in safety alignment techniques, safeguarding LLMs during adaptation to various tasks remains a challenge.
Approach: They propose a framework to quantify how different parameters affect LLM safety . they propose two targeted intervention paradigms for safety enhancement and preservation .
Outcome: The proposed framework reveals safety-critical patterns across different LLM architectures.
Rapid Diffusion: Building Domain-Specific Text-to-Image Synthesizers with Fast Inference Speed (2023.acl-industry)

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Challenge: Text-to-Image Synthesis (TIS) aims to generate images based on textual inputs . but, current diffusion-based models lack entity knowledge and low inference speed .
Approach: They propose a framework for training and deploying latent diffusion models with rich entity knowledge injected and optimized networks.
Outcome: The proposed framework improves image quality and inference speed and can be used in industrial applications.

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