Resources
Unleashing Generative AI for Business Growth
Discover curated resources to master data engineering and AI innovations.
Measuring Massive Multitask Language Understanding (MMLU)
This paper presents a new test to measure multitask accuracy in text models, highlighting the need for substantial improvements in achieving expert-level accuracy and addressing lopsided performance and low accuracy on socially important subjects.
BigBench-Hard – Beyond the Imitation Game: Quantifying and Extrapolating the Capabilities of Language Models
The paper introduces BIG-bench, a benchmark for evaluating language models on challenging tasks, providing insights on scale, calibration, and social bias.
Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
This paper provides a systematic overview of Parameter-Efficient Fine-tuning (PEFT) Methods in all three categories discussed in the lecture videos.
General Language Understanding Evaluation (GLUE) benchmark
This paper introduces GLUE, a benchmark for evaluating models on diverse natural language understanding (NLU) tasks and emphasizing the importance of improved general NLU systems.
SuperGLUE
This paper introduces SuperGLUE, a benchmark designed to evaluate the performance of various NLP models on a range of challenging language understanding tasks.
ROUGE: A Package for Automatic Evaluation of Summaries
This paper introduces and evaluates four different measures (ROUGE-N, ROUGE-L, ROUGE-W, and ROUGE-S) in the ROUGE summarization evaluation package, which assess the quality of summaries by comparing them to ideal human-generated summaries.
Generative AI on AWS
Building Context-Aware Multimodal Reasoning Applications
Scaling Laws for Neural Language Models
empirical study by researchers at OpenAI exploring the scaling laws for large language models.
BloombergGPT: A Large Language Model for Finance
LLM trained specifically for the finance domain, a good example that tried to follow chinchilla laws.
Scaling Instruction-Finetuned Language Models
Scaling fine-tuning with a focus on task, model size and chain-of-thought data.
Introducing FLAN: More generalizable Language Models with Instruction Fine-Tuning
his blog (and article) explores instruction fine-tuning, which aims to make language models better at performing NLP tasks with zero-shot inference.
HELM – Holistic Evaluation of Language Models
HELM is a living benchmark to evaluate Language Models more transparently.
The Data Warehouse Toolkit
The internals of Apache Spark
Welcome to The Internals of Spark Core online book!
Azure Data Engineering Courses
Microsoft Certified: Azure Data Engineer Associate
On the Effectiveness of Parameter-Efficient Fine-Tuning
The paper analyzes sparse fine-tuning methods for pre-trained models in NLP.
LoRA Low-Rank Adaptation of Large Language Models
This paper proposes a parameter-efficient fine-tuning method that makes use of low-rank decomposition matrices to reduce the number of trainable parameters needed for fine-tuning language models.
QLoRA: Efficient Finetuning of Quantized LLMs
This paper introduces an efficient method for fine-tuning large language models on a single GPU, based on quantization, achieving impressive results on benchmark tests.
The Power of Scale for Parameter-Efficient Prompt Tuning
The paper explores “prompt tuning,” a method for conditioning language models with learned soft prompts, achieving competitive performance compared to full fine-tuning and enabling model reuse for many tasks.
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