Universal and Transferable Adversarial Attacks on Aligned Language Models

Publié le par

https://llm-attacks.org/

This research examines the safety of large language models (LLMs) such as ChatGPT, Bard, and Claude. It demonstrates the potential for automated creation of adversarial attacks, using character sequences added to user queries that manipulate the LLM into following harmful commands. Unlike traditional « jailbreaks, » these attacks are automated and can affect both open-source and closed-source chatbots. The study raises concerns about the effectiveness of mitigation measures and suggests that the challenges posed by adversarial behavior might persist due to the nature of deep learning models. The findings highlight the need for careful consideration of the safety implications as LLMs become more integrated into various applications.

Publié dans LLM

Time Series Made Easy in Python: DARTS

Publié le par

Darts is a Python library for user-friendly forecasting and anomaly detection on time series. It contains a variety of models, from classics such as ARIMA to deep neural networks.

Some of the key features of Darts include:

  • A simple and intuitive interface for defining and fitting models
  • Support for different types of time series data, including univariate, multivariate, and panel data
  • A wide range of built-in models, including ARIMA, Exponential Smoothing, Prophet, LSTM, and TCN
  • Tools for hyperparameter tuning and model selection, such as cross-validation and grid search
  • Visualization tools for exploring and analyzing time series data and model outputs

Library

ModelUnivariateMultivariateProbabilisticMultiple series (global)Past-observed covariatesFuture-known covariatesStatic covariatesReference
ARIMA✅✅✅
VARIMA✅✅✅
AutoARIMA✅✅
StatsForecastAutoARIMA (faster AutoARIMA)✅✅✅Nixtla’s statsforecast
ExponentialSmoothing✅✅
StatsForecastETS✅✅Nixtla’s statsforecast
BATS and TBATS✅✅TBATS paper
Theta and FourTheta✅Theta & 4 Theta
Prophet (see install notes)✅✅✅Prophet repo
FFT (Fast Fourier Transform)✅
KalmanForecaster using the Kalman filter and N4SID for system identification✅✅✅✅N4SID paper
Croston method✅
RegressionModel; generic wrapper around any sklearn regression model✅✅✅✅✅✅
RandomForest✅✅✅✅✅✅
LinearRegressionModel✅✅✅✅✅✅✅
LightGBMModel✅✅✅✅✅✅✅
CatBoostModel✅✅✅✅✅✅✅
XGBModel✅✅✅✅✅✅✅
RNNModel (incl. LSTM and GRU); equivalent to DeepAR in its probabilistic version✅✅✅✅✅DeepAR paper
BlockRNNModel (incl. LSTM and GRU)✅✅✅✅✅
NBEATSModel✅✅✅✅✅N-BEATS paper
NHiTSModel✅✅✅✅✅N-HiTS paper
TCNModel✅✅✅✅✅TCN paper, DeepTCN paper, blog post
TransformerModel✅✅✅✅✅
TFTModel (Temporal Fusion Transformer)✅✅✅✅✅✅✅TFT paper, PyTorch Forecasting
DLinearModel✅✅✅✅✅✅✅DLinear paper
NLinearModel✅✅✅✅✅✅✅NLinear paper
Naive Baselines✅✅

Category Encoders

Publié le par

A set of scikit-learn-style transformers for encoding categorical variables into numeric with different techniques.

Category Encoders is a Python library for encoding categorical variables for machine learning tasks. It is available on contrib.scikit-learn.org and extends the capabilities of scikit-learn’s preprocessing module.

The library provides several powerful encoding techniques for dealing with categorical data, including:

  • Ordinal encoding: maps categorical variables to integer values based on their order of appearance
  • One-hot encoding: creates a binary feature for each category in a variable
  • Binary encoding: maps each category to a binary code
  • Target encoding: encodes each category with the mean target value for that category
  • Hashing encoding: maps each category to a random index in a hash table

Category Encoders also supports a range of advanced features, such as handling missing values, combining multiple encoders, and applying encoders to specific subsets of features.

Overall, Category Encoders is a useful tool for preprocessing categorical data and improving the accuracy and performance of machine learning models.

Yellowbrick: Machine Learning Visualization

Publié le par
https://www.scikit-yb.org/en/latest/

Feature Visualization

Classification Visualization

Regression Visualization

Clustering Visualization

Model Selection Visualization

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

TabTransformer: Tabular Data Modeling Using Contextual Embeddings

Publié le par

The main idea in the paper is that the performance of regular Multi-layer Perceptron (MLP) can be significantly improved if we use Transformers to transforms regular categorical embeddings into contextual ones.

The TabTransformer is built upon self-attention based Transformers. The Transformer layers transform the embed- dings of categorical features into robust contextual embed- dings to achieve higher prediction accuracy.