The Responsible Generative AI Library - Pacific AI

Holistic Evaluation of Large Language Models: Assessing Robustness, Accuracy, and Toxicity for Real-World Applications

LangTest: A comprehensive evaluation library for custom LLM and NLP models

Identifying and Mitigating Bias in AI Models for Recruiting

Automated Testing of Bias, Fairness, and Robustness of Generative AI Solutions

Building Responsible Language Models with the LangTest Library

The Ethical Implications of Medical LLMs in Healthcare

LangTest: Unveiling & Fixing Biases with End-to-End NLP Pipelines

Automatically Testing for Demographic Bias in Clinical Treatment Plans Generated by Large Language Models

Beyond Accuracy: Robustness Testing of Named Entity Recognition Models with LangTest

Elevate Your NLP Models with Automated Data Augmentation for Enhanced Performance

Automating Responsible AI: Integrating Hugging Face and LangTest for More Robust Models

Mitigating Gender-Occupational Stereotypes in AI: Evaluating Language Models with the Wino Bias Test through the Langtest Library

Detecting and Evaluating Sycophancy Bias: An Analysis of LLM and AI Solutions

Unmasking Language Model Sensitivity in Negation and Toxicity Evaluations

Unveiling Bias in Language Models: Gender, Race, Disability, and Socioeconomic Perspectives

Unmasking the Biases Within AI: How Gender, Ethnicity, Religion, and Economics Shape NLP and Beyond

Evaluating Large Language Models on Gender-Occupational Stereotypes Using the Wino Bias Test

Streamlining ML Workflows: Integrating MLFlow Tracking with LangTest for Enhanced Model Evaluations

Testing the Question Answering Capabilities of Large Language Models

Evaluating Stereotype Bias with LangTest

John Snow Labs Announces Fifth Annual NLP Summit, the World’s Largest Gathering for the Applied NLP, LLM, and Generative AI Community