https://cameronrwolfe.substack.com/p/practical-prompt-engineering-part

• Prompt engineering: An empirical science focused on optimizing LLM (Large Language Model) performance through various prompting strategies.
• Aims to understand prompting mechanics and employs techniques to enhance LLM capabilities.
• Zero/few-shot learning: A fundamental technique where LLMs perform tasks with minimal or no training examples, showcasing their remarkable adaptability.
• Instruction prompting: Another vital technique involving explicit instructions in prompts to guide LLM behavior.
• Overview intends to impart practical insights and strategies for effective prompt engineering and LLM utilization.
• Provides actionable tricks and takeaways for prompt engineers and LLM practitioners to enhance their effectiveness.

https://cameronrwolfe.substack.com/p/the-history-of-open-source-llms-early

• Language modeling research traces back to models like GPT, GPT-2, and pre-transformer methods such as ULMFit.
• GPT-3’s proposal marked the initial rise in popularity by showcasing impressive few-shot learning through self-supervised pre-training and in-context learning.
• The recognition of GPT-3 led to the creation of various large language models (LLMs), including InstructGPT and ChatGPT, sparking widespread interest in generative AI.
• Early LLMs often remained closed source, limiting researchers’ understanding and improvement of their workings.
• Open-source variants of popular language models began to emerge gradually, although they initially lagged behind proprietary models in performance.
• These early open-source models laid the groundwork for increased transparency in LLM research and inspired the development of more potent subsequent models like Falcon and LLaMA-21.
• The overview is part of a three-part series that delves into the history of open-source language models, exploring their beginnings, recent developments, and the application of imitation and alignment techniques to enhance their performance.

cleanlab automatically detects problems in a ML dataset. This data-centric AI package facilitates machine learning with messy, real-world data by providing clean labels for robust training and flagging errors in your data

Paper: https://arxiv.org/pdf/1911.00068.pdf

Code : Code

Feature Visualization

• Rank Features: pairwise ranking of features to detect relationships
• Parallel Coordinates: horizontal visualization of instances
• Radial Visualization: separation of instances around a circular plot
• PCA Projection: projection of instances based on principal components
• Manifold Visualization: high dimensional visualization with manifold learning
• Joint Plots: direct data visualization with feature selection

Classification Visualization

• Class Prediction Error: shows error and support in classification
• Classification Report: visual representation of precision, recall, and F1
• ROC/AUC Curves: receiver operator characteristics and area under the curve
• Precision-Recall Curves: precision vs recall for different probability thresholds
• Confusion Matrices: visual description of class decision making
• Discrimination Threshold: find a threshold that best separates binary classes

Regression Visualization

• Prediction Error Plot: find model breakdowns along the domain of the target
• Residuals Plot: show the difference in residuals of training and test data
• Alpha Selection: show how the choice of alpha influences regularization
• Cook’s Distance: show the influence of instances on linear regression

Clustering Visualization

• K-Elbow Plot: select k using the elbow method and various metrics
• Silhouette Plot: select k by visualizing silhouette coefficient values
• Intercluster Distance Maps: show relative distance and size/importance of clusters

Model Selection Visualization

• Validation Curve: tune a model with respect to a single hyperparameter
• Learning Curve: show if a model might benefit from more data or less complexity
• Feature Importances: rank features by importance or linear coefficients for a specific model
• Recursive Feature Elimination: find the best subset of features based on importance

Target Visualization

• Balanced Binning Reference: generate a histogram with vertical lines showing the recommended value point to bin the data into evenly distributed bins
• Class Balance: see how the distribution of classes affects the model
• Feature Correlation: display the correlation between features and dependent variables

Text Visualization

• Term Frequency: visualize the frequency distribution of terms in the corpus
• t-SNE Corpus Visualization: use stochastic neighbor embedding to project documents
• Dispersion Plot: visualize how key terms are dispersed throughout a corpus
• UMAP Corpus Visualization: plot similar documents closer together to discover clusters
• PosTag Visualization: plot the counts of different parts-of-speech throughout a tagged corpus

Text using Chatgpt, image from Dall-E, text to speech from D-ID