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Data Party THU
Data Party THU
Mar 25, 2026 · Artificial Intelligence

How Knowledge‑Guided Context Optimization Boosts Zero‑Shot Vision‑Language Models

The article analyzes the Base‑to‑New generalization problem of CLIP‑based visual‑language models, explains why standard prompt tuning (CoOp) forgets base knowledge, and presents the KgCoOp framework that adds a knowledge‑guided loss to keep learned prompts close to hand‑crafted ones, dramatically improving unseen‑class performance while preserving efficiency.

CLIPGeneralizationKnowledge-guided Optimization
0 likes · 12 min read
How Knowledge‑Guided Context Optimization Boosts Zero‑Shot Vision‑Language Models
DeepHub IMBA
DeepHub IMBA
Mar 23, 2026 · Artificial Intelligence

How KgCoOp Uses Knowledge‑Guided Context Optimization to Prevent Prompt Tuning Forgetting

The article analyzes why standard prompt tuning (CoOp) causes catastrophic forgetting in visual‑language models, introduces the KgCoOp framework that adds a knowledge‑guided loss to regularize prompts, and shows through extensive experiments on 11 benchmarks that KgCoOp improves unseen‑class accuracy, harmonic mean, and efficiency while discussing trade‑offs and limitations.

Catastrophic ForgettingKnowledge-guided OptimizationPrompt Tuning
0 likes · 11 min read
How KgCoOp Uses Knowledge‑Guided Context Optimization to Prevent Prompt Tuning Forgetting
AI Frontier Lectures
AI Frontier Lectures
Jan 27, 2026 · Artificial Intelligence

A Unified Framework for Neural Network Reprogrammability: From Model Reprogramming to Prompt Tuning

This article surveys recent advances in neural network reprogrammability, presenting a unified framework that categorizes model reprogramming, prompt tuning, prompt instruction, and in‑context learning, highlights the shift from parameter‑centric to reprogrammability‑centric adaptation, and provides efficiency analyses, taxonomy, and practical case studies.

Model AdaptationNeural Network ReprogrammabilityPrompt Tuning
0 likes · 16 min read
A Unified Framework for Neural Network Reprogrammability: From Model Reprogramming to Prompt Tuning
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Aug 23, 2025 · Artificial Intelligence

Why LoRA, QLoRA, Prompt & Prefix Tuning Are Changing Large‑Model Fine‑Tuning

This article explains the mathematical basis of LoRA, compares it with QLoRA, Prompt Tuning, Prefix Tuning and P‑tuning, shows practical PyTorch implementations, and provides mixed‑precision training tips so readers can choose the most memory‑efficient fine‑tuning method for their large language models.

LoRAPrompt TuningQLoRA
0 likes · 17 min read
Why LoRA, QLoRA, Prompt & Prefix Tuning Are Changing Large‑Model Fine‑Tuning
Data Thinking Notes
Data Thinking Notes
Jun 15, 2025 · Artificial Intelligence

Mastering Fine-Tuning: From Basics to Advanced Techniques for Large Language Models

Fine‑tuning transforms a general‑purpose large language model into a domain‑specific expert by training on a small, labeled dataset, and this guide explains its background, core concepts, technical mechanisms, various methods—including full‑parameter, LoRA, adapters, and prompt tuning—plus practical use cases, advantages, challenges, and best‑practice recommendations.

AIAdapterLoRA
0 likes · 13 min read
Mastering Fine-Tuning: From Basics to Advanced Techniques for Large Language Models
Alimama Tech
Alimama Tech
Apr 23, 2025 · Artificial Intelligence

Distribution-aware Graph Prompt Tuning (DAGPrompT) for Heterophilic Graphs

Distribution‑aware Graph Prompt Tuning (DAGPrompT) tackles the pre‑training/downstream mismatch on heterophilic graphs by jointly applying low‑rank GLoRA adaptation and hop‑specific prompts that recast tasks as link‑prediction, yielding up to 4.79% accuracy gains and an average 2.43% improvement in few‑shot node classification.

Few‑Shot LearningPrompt Tuningdistribution-aware
0 likes · 9 min read
Distribution-aware Graph Prompt Tuning (DAGPrompT) for Heterophilic Graphs
NewBeeNLP
NewBeeNLP
May 20, 2024 · Artificial Intelligence

How RecGPT Leverages ChatGPT‑Style Prompt Tuning for Better Sequential Recommendation

RecGPT applies a ChatGPT‑like pre‑training and personalized prompt‑tuning paradigm to sequential recommendation, introducing a two‑stage recall mechanism that improves offline HR/NDCG metrics and yields modest online interaction gains in a real‑world short‑video platform.

Prompt TuningRecGPTauto-regressive pretraining
0 likes · 8 min read
How RecGPT Leverages ChatGPT‑Style Prompt Tuning for Better Sequential Recommendation
ByteDance Web Infra
ByteDance Web Infra
Jun 16, 2023 · Artificial Intelligence

How AIGC Transforms Document Search: Architecture, Techniques, and Future Directions

This article explains how AI‑generated content (AIGC) reshapes document search by combining traditional indexing with modern embedding and prompt‑tuning techniques, reviews key components such as LangChain and Supabase, compares existing AI‑search products, and discusses the future blend of classic and AI‑driven search.

AI searchAIGCEmbedding
0 likes · 15 min read
How AIGC Transforms Document Search: Architecture, Techniques, and Future Directions
Kuaishou Tech
Kuaishou Tech
Apr 24, 2023 · Artificial Intelligence

Divide‑and‑Conquer Embedding‑Based Retrieval with Prompt‑Based Multi‑Task Learning for Large‑Scale Recommendation

This paper identifies the trade‑off between simple and hard negatives in embedding‑based retrieval for recommendation, proposes a clustering‑based divide‑and‑conquer framework combined with prompt‑driven multi‑task learning to improve relevance, diversity, and fairness, and validates the approach through offline metrics, online A/B tests, and comparative experiments.

Embedding RetrievalPrompt Tuningapproximate nearest neighbor
0 likes · 9 min read
Divide‑and‑Conquer Embedding‑Based Retrieval with Prompt‑Based Multi‑Task Learning for Large‑Scale Recommendation
Sohu Tech Products
Sohu Tech Products
Mar 22, 2023 · Artificial Intelligence

An Overview of Prompt Learning in Natural Language Processing

This article reviews the evolution of NLP training paradigms, explains why prompt learning is needed, defines its core concepts, and surveys major hard‑template and soft‑template methods such as PET, LM‑BFF, P‑tuning, and Prefix‑tuning, highlighting their advantages for few‑shot and zero‑shot scenarios.

Few-ShotNLPPrompt Tuning
0 likes · 10 min read
An Overview of Prompt Learning in Natural Language Processing
AsiaInfo Technology: New Tech Exploration
AsiaInfo Technology: New Tech Exploration
Feb 20, 2023 · Industry Insights

Why Pre‑trained Large Models Are the New Infrastructure for AI Applications

Pre‑trained large models are emerging as the foundational infrastructure for AI across industries; this article analyzes their technical advantages, application trends in NLP, CV and multimodal domains, presents a telecom customer‑service case study with performance benchmarks, and outlines future deployment challenges and research directions.

Computer VisionNLPPrompt Tuning
0 likes · 23 min read
Why Pre‑trained Large Models Are the New Infrastructure for AI Applications
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Dec 12, 2022 · Artificial Intelligence

How Unified Prompt Tuning Boosts Few-Shot NLP Performance Across Tasks

Unified Prompt Tuning (UPT) is a meta-learning based few‑shot algorithm that converts diverse NLP tasks into a common Prompt‑Options‑Verbalizer format, enabling large pre‑trained language models to achieve higher accuracy with minimal labeled data, as demonstrated on EMNLP‑2022 benchmarks and SuperGLUE datasets.

Few‑Shot LearningMeta LearningNLP
0 likes · 10 min read
How Unified Prompt Tuning Boosts Few-Shot NLP Performance Across Tasks
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Nov 11, 2022 · Artificial Intelligence

Language Model as a Service and Black‑Box Optimization: Insights from Prof. Qiu Xipeng’s Talk

Prof. Qiu Xipeng’s talk highlighted how large language models can be offered as a service and efficiently adapted via in‑context learning, lightweight label‑tuning, and gradient‑free black‑box optimization, showcasing a unified asymmetric Transformer (CPT) that handles understanding, generation, ABSA and NER tasks while reducing resource demands.

Black-Box OptimizationLLMLanguage Model
0 likes · 15 min read
Language Model as a Service and Black‑Box Optimization: Insights from Prof. Qiu Xipeng’s Talk