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A reading list for papers on causality for natural language processing (NLP)
Mechanistic Interpretability Visualizations using React
Automated Identification of Redundant Layer Blocks for Pruning in Large Language Models
allRank is a framework for training learning-to-rank neural models based on PyTorch.
Representation Engineering: A Top-Down Approach to AI Transparency
A library for mechanistic interpretability of GPT-style language models
The papers are organized according to our survey: Evaluating Large Language Models: A Comprehensive Survey.
Package for causal inference in graphs and in the pairwise settings. Tools for graph structure recovery and dependencies are included.
Neuro-symbolic approaches to reasoning problems from abstract argumentation
Reentrant Incremental Argumentation Framework API and solver
ORLA is a symbolic reinforcement learning approach that learns a value-based argumentation framework as a reasoning engine for solving a task. This repo demonstrates ORLA on both the Foggy Frozen L…
Prolog Framework for Bipolar Abstract Argumentation
Structure Learning of Gradual Bipolar Argumentation Graphs using Genetic Algorithms
Code for "Neural causal learning from unknown interventions"
Example causal datasets with consistent formatting and ground truth
cplint is a suite of programs for reasoning with probabilistic logic programs
The easiest way to use deep metric learning in your application. Modular, flexible, and extensible. Written in PyTorch.
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphic…
Implementation of Gaussian Mixture Variational Autoencoder (GMVAE) for Unsupervised Clustering
Code for Transformer Hawkes Process, ICML 2020.
Causal discovery for time series