In-context Learning Courses and Certifications

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Instruction Fine-Tuning and In-Context Learning of Large Language Models with Symbols

Delve into advanced concepts of LLM instruction fine-tuning and in-context learning, exploring symbol tuning, FLAN-PaLM models, and cutting-edge research developments.

• YouTube
• 1 hour 4 minutes
• On-Demand
• Free Video

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Fine-tuning ChatGPT with In-Context Learning - Chain of Thought, AMA, and ReAct Reasoning

Master advanced in-context learning techniques like Chain-of-Thought and ReAct to optimize ChatGPT's performance without expensive fine-tuning, based on latest research insights and practical applications.

• YouTube
• 37 minutes
• On-Demand
• Free Video

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Dirichlet Energy Minimization Explains In-Context Learning

Explore groundbreaking Harvard research on how Dirichlet Energy Minimization explains in-context learning in LLMs, optimizing RAG systems and transformer learning without expensive fine-tuning.

• YouTube
• 29 minutes
• On-Demand
• Free Video

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Better In-Context Learning and RAG System Optimization

Dive into cutting-edge research on optimizing Large Language Models through improved In-Context Learning and RAG systems, without expensive fine-tuning or pre-training procedures.

• YouTube
• 27 minutes
• On-Demand
• Free Video

Harvard CMSA

What Learning Algorithm is In-Context Learning? - Understanding Transformer Models and Neural Sequence Learning

Delve into the algorithmic foundations of in-context learning in transformers, exploring how neural networks implement implicit learning mechanisms through activation patterns and predictive modeling.

• YouTube
• 50 minutes
• On-Demand
• Free Video

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In-Context Learning - Comparing Extreme Context Length with Fine-Tuning and RAG

Explore groundbreaking research on in-context learning with extended context models, comparing performance against fine-tuning and RAG methods while examining scaling impacts and demonstration effectiveness.

• YouTube
• 22 minutes
• On-Demand
• Free Video

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Unsupervised In-Context Learning Plus: From Few-Shot to Many-Shot Learning in Large Language Models

Explore advanced In-Context Learning techniques for Large Language Models, from few-shot to many-shot approaches, focusing on unsupervised learning and autonomous capabilities with 1M token context.

• YouTube
• 31 minutes
• On-Demand
• Free Video

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Why AI In-Context Learning Works - Explained

Explore how in-context learning functions within transformer architectures and its relationship to fine-tuning through weight tensor dynamics and implicit learning algorithms.

• YouTube
• 31 minutes
• On-Demand
• Free Video

Center for Language & Speech Processing(CLSP), JHU

ICL Ciphers - Quantifying Learning in In-Context Learning via Substitution Ciphers

Explore how large language models learn through substitution ciphers that test their ability to decode patterns and distinguish task retrieval from inference-time learning.

• YouTube
• 12 minutes
• On-Demand
• Free Video

AutoML Seminars

Is Mamba Capable of In-Context Learning? - An Analysis of State Space Models

Explore how Mamba, a state space model, demonstrates in-context learning capabilities comparable to transformers while handling longer input sequences more efficiently in AI applications.

• YouTube
• 44 minutes
• On-Demand
• Free Video

echohive

LLM-Driven In-Context Adaptive Learning - New Attempt

Discover how to implement LLM-driven adaptive learning systems that dynamically adjust content based on context for personalized educational experiences.

• YouTube
• 15 minutes
• On-Demand
• Free Video

Paul G. Allen School

Data, Architecture and Algorithms in In-Context Learning

Explore data, architecture, and algorithms driving in-context learning through expert insights from University of Michigan researcher in this IFDS workshop.

• YouTube
• 51 minutes
• On-Demand
• Free Video

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Beyond RAG: Continual Learning of Large Language Models with InCA

Discover how InCA enables continuous learning in LLMs through innovative in-context learning and external modules, offering an alternative to traditional RAG and fine-tuning approaches.

• YouTube
• 40 minutes
• On-Demand
• Free Video

Stanford Seminar 2022 - Transformer Circuits, Induction Heads, In-Context Learning

Explore reverse engineering of transformer language models, focusing on induction head circuits and their role in in-context learning. Gain insights into neural network interpretability and AI safety.

• YouTube
• 1 hour
• On-Demand
• Free Video

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Demonstrate-Search-Predict: LLM Pipeline to Retriever Model

Explore Stanford's innovative DSP methodology for integrating LLMs with retrieval models, featuring self-programmable pipelines and advanced graph-based architectures for complex NLP tasks.

• YouTube
• 27 minutes
• On-Demand
• Free Video