I have a free intro course on Natural Language Processing and Large Language Models here that might be useful: https://
lightning.ai/pages/courses/
deep-learning-fundamentals/unit-8.0-natural-language-processing-and-large-language-models/
… I also have a bunch of writings here (Including a list of papers I recommend): https://
magazine.sebastianraschka.com
LLMS
-
Free NLP and Large Language Models Course with Recommended Writings
By
–
-

Speech-to-Text Adapter for Enhanced LLM Speech Understanding
By
–
Speech-to-Text Adapter and Speech-to-Entity Retriever Augmented LLMs for Speech Understanding paper page: https://
huggingface.co/papers/2306.07
944
… Large Language Models (LLMs) have been applied in the speech domain, often incurring a performance drop due to misaligned between speech and -

GPT-Calls: Synthetic Conversation Generation for Call Segmentation
By
–
GPT-Calls: Enhancing Call Segmentation and Tagging by Generating Synthetic Conversations via Large Language Models paper page: https://
huggingface.co/papers/2306.07
941
… Transcriptions of phone calls are of significant value across diverse fields, such as sales, customer service, healthcare, and -
SayTap: Language to Quadrupedal Locomotion Control
By
–
SayTap: Language to Quadrupedal Locomotion
— AK (@_akhaliq) 14 juin 2023
paper page: https://t.co/Dk14Ds1D94
Large language models (LLMs) have demonstrated the potential to perform high-level planning. Yet, it remains a challenge for LLMs to comprehend low-level commands, such as joint angle targets or… pic.twitter.com/BteEUxEmalSayTap: Language to Quadrupedal Locomotion paper page: https://
huggingface.co/papers/2306.07
580
… Large language models (LLMs) have demonstrated the potential to perform high-level planning. Yet, it remains a challenge for LLMs to comprehend low-level commands, such as joint angle targets or -
Behind-the-scenes training details of Flan-T5 model revealed
By
–
Insightful thread by @hwchung27 that shares some behind the scenes of training flan-t5. These details are often overlooked and sometimes mentioned in footnotes in papers (or not mentioned at all, i.e., "not paper worthy"). Sometimes they are stack dependent, model dependent or
-

TART: Plug-and-Play Transformer Module for Task-Agnostic Reasoning
By
–
TART: A plug-and-play Transformer module for task-agnostic reasoning paper page: https://
huggingface.co/papers/2306.07
536
… Large language models (LLMs) exhibit in-context learning abilities which enable the same model to perform several tasks without any task-specific training. In contrast, -

arXiVeri: Automatic Table Verification with GPT Technology
By
–
arXiVeri: Automatic table verification with GPT paper page: https://
huggingface.co/papers/2306.07
968
… Without accurate transcription of numerical data in scientific documents, a scientist cannot draw accurate conclusions. Unfortunately, the process of copying numerical data from one paper to -

Stanford Course on Multi-Task Learning and Meta-Learning
By
–
Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022 – YouTube
https://bit.ly/3Mpa9TY
#AI #MachineLearning #DeepLearning #LLMs #DataScience -

Annual State Of Data Quality Survey 2023
By
–
The Annual State Of Data Quality Survey, 2023 https://
bit.ly/3Mt9dhM #AI #MachineLearning #DeepLearning #LLMs #DataScience -

SlimPajama Reduces AI Training Compute Costs by 50 Percent
By
–
Why we built SlimPajama – it's all about training efficiency. Without de-duplication, a model would have to go through 1.2T tokens before seeing ~600B unique tokens. SlimPajama sees 600B tokens in half the time. That saves you 50% on compute costs! https://
cerebras.net/blog/slimpajam
a-a-627b-token-cleaned-and-deduplicated-version-of-redpajama
…