Pre Training Articles

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8/17/2024 • EN

New LLM Pre-training and Post-training Paradigms

Analyzes the latest pre-training and post-training methodologies used in state-of-the-art LLMs like Qwen 2, Apple's models, Gemma 2, and Llama 3.1.

Fine Tuning Language Models llm Post Training Pre Training

Sebastian Raschka

8/17/2024 • EN

New LLM Pre-training and Post-training Paradigms

A technical review of the latest pre-training and post-training methodologies used in state-of-the-art large language models (LLMs) like Qwen 2 and Llama 3.1.

ai large language models llm Post Training Pre Training

Sebastian Raschka

7/7/2024 • EN

Extrinsic Hallucinations in LLMs

Explores the causes and types of hallucinations in large language models, focusing on extrinsic hallucinations and how training data affects factual accuracy.

Factuality Fine Tuning Hallucination llm Pre Training

Lilian Weng

12/5/2021 • EN

Learning with not Enough Data Part 1: Semi-Supervised Learning

Explores semi-supervised learning techniques for training models when labeled data is scarce, focusing on combining labeled and unlabeled data.

Data Scarcity Fine Tuning Machine Learning Pre Training Semi Supervised Learning

Lilian Weng

Pre Training Articles

New LLM Pre-training and Post-training Paradigms

New LLM Pre-training and Post-training Paradigms

Extrinsic Hallucinations in LLMs

Learning with not Enough Data Part 1: Semi-Supervised Learning

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