alexkoulakos/explain-then-predict
Source code for the BlackBoxNLP 2024 @ EMNLP paper "Enhancing adversarial robustness in Natural Language Inference using explanations"
This project helps Natural Language Processing (NLP) researchers and practitioners improve the reliability of their Natural Language Inference (NLI) models. It takes a pair of text sentences (a premise and a hypothesis) and evaluates if providing an intermediate explanation of their relationship makes the model more robust against subtly altered, misleading inputs. The output indicates whether the two sentences entail, contradict, or are neutral to each other, with increased confidence.
No commits in the last 6 months.
Use this if you are developing or evaluating NLI models and want to make them more resistant to adversarial attacks and deceptive text inputs.
Not ideal if you are looking for a general-purpose NLI model or a tool for tasks other than evaluating model robustness through explanations.
Stars
10
Forks
—
Language
Python
License
MIT
Category
Last pushed
Nov 18, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/generative-ai/alexkoulakos/explain-then-predict"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
sdv-dev/SDV
Synthetic data generation for tabular data
sdv-dev/SDGym
Benchmarking synthetic data generation methods.
NVIDIA-NeMo/DataDesigner
🎨 NeMo Data Designer: A general library for generating high-quality synthetic data from scratch...
AlexanderVNikitin/tsgm
Generation and evaluation of synthetic time series datasets (also, augmentations,...
mostly-ai/mostlyai
Synthetic Data SDK ✨