ashishsheth (Ashish Sheth)

upvoted a paper 8 days ago

The Common Pile v0.1: An 8TB Dataset of Public Domain and Openly Licensed Text

Paper • 2506.05209 • Published Jun 5 • 46

upvoted 2 articles 12 days ago

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KV Caching Explained: Optimizing Transformer Inference Efficiency

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Getting Started With Hugging Face in 10 Minutes

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reacted to Kseniase's post with 🔥❤️ 27 days ago

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6 Essential Reads on core AI/ML topics:

Time to look at some free useful resources that can help you upgrade your knowledge of AI and machine learning!
Today we offer you these 6 must-read surveys that can be your perfect guides to the major fields and techniques:

1. Foundations of Large Language Models by Tong Xiao and Jingbo Zhu → https://arxiv.org/abs/2501.09223
Many recommend this 270-page book as a good resource to focus on fundamental concepts, such as pre-training, generative models, prompting, alignment, and inference

2. Large Language Models Post-Training: Surveying Techniques from Alignment to Reasoning -> A Survey on Post-training of Large Language Models (2503.06072)
Read this to master policy optimization (RLHF, DPO, GRPO), supervised and parameter-efficient fine-tuning, reasoning, integration, and adaptation techniques

3. Agentic Large Language Models, a survey by Leiden University → https://arxiv.org/abs/2503.23037
Surveys agentic LLMs across reasoning, tools, and multi-agent collaboration, highlighting their synergy. It also explores their promise, risks and applications in medicine, finance, science.

4. A Survey of Context Engineering for Large Language Models → A Survey of Context Engineering for Large Language Models (2507.13334)
Defines Context Engineering as systematic info design for LLMs beyond prompting, covering retrieval, processing, management, and architectures like RAG and multi-agent systems

5. A Survey of Generative Categories and Techniques in Multimodal Large Language Models → https://arxiv.org/abs/2506.10016
Covers multimodal models, exploring six generative modalities, key techniques (SSL, RLHF, CoT), architectural trends, and challenges

6. Large Language models for Time Series Analysis: Techniques, Applications, and Challenges → https://arxiv.org/abs/2506.11040
Explains how LLMs transform time series analysis by enhancing pattern recognition and long-term dependency handling + shows how to build them

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