Increasingly, more and more people are turning to large language models (LLMs) for healthcare advice and consultation, making it important to gauge the efficacy and accuracy of the responses of LLMs to such queries. While there are pre-existing medical benchmarks literature which seeks to accomplish this very task, these benchmarks are almost universally in English, which has led to a notable gap in existing literature pertaining to multilingual LLM evaluation. Within this work, we seek to aid in addressing this gap with Cancer-Myth-Indic, an Indic language benchmark built by translating a 500-item subset of Cancer-Myth, sampled evenly across its original categories, into five under-served but widely used languages from the subcontinent (500 per language; 2,500 translated items total). Native-speaker translators followed a style guide for preserving implicit presuppositions in translation; items feature false presuppositions relating to cancer. We evaluate several popular LLMs under this presupposition stress.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! These days, conversational artificial intelligence (AI) chatbots are everywhere on websites, SMS and social channels. Conversational AI-supported chatbots that use natural language processing (NLP) help customers deal with everything from product recommendations to order questions. Enterprises love conversational AI chatbots, too: According to a recent Gartner report, by 2027 chatbots will become the primary customer service channel for roughly a quarter of organizations.

Writer is an AI-powered tool for checking and guiding content creators in organizations where voice and branding are essential. Its new feature CoWrite does that writing itself -- but don't worry, this isn't quite the content apocalypse we've been worried about. CoWrite is the latest in a new wave of tools that use large language models like GPT-3, but modify them using "fine tuning," a common phrase but with a special meaning in the machine learning world. Basically it means giving the big, general model a specific set of content to imitate more closely than the rest of the language it understands -- a bit like telling an image creation model to make a picture in a certain style by feeding it examples. Writer's tools already do this to a certain extent, ingesting style guides and other data to provide a live style-check service: "use this preferred word instead of that," or "use active voice in headlines," depending on what your organization likes.

Experienced programmers understand perfectly well that in development they spend most of the time reading code and therefore they treat the process of writing code with the deepest trepidation (and sometimes with fanaticism). To write quality and maintainable code, you need to take the time to write tests and integrate QA tools. There is a whole technique aimed at test-driven development (TDD) and I will not devote this article to the topic of testing as such. Tests are absolutely necessary and there is nothing to discuss. In this article, we are going to talk about tools that help you write quality Python code.

For many of these steps, there are no real short cuts to be taken. The only way to build a minimum viable product, for example, is to roll up your sleeves and start coding. However, in a few cases, tools exist to automate tedious manual processes and make your life much easier. In Python, this is the situation for steps 4, 8 and 10, thanks to the unittest, flake8 and sphinx packages. Let's look at each of these packages one by one.

This style guide is meant for use by advanced beginner to advanced intermediate developers of scientific code in Python. The term LowClass Python hints at reducing the use of object oriented design. It is an attempt to be as witty as Tom Anderson when he coined the term C -- (C plus-plus, minus-minus). You see, C is a very rich language with many features. This allows a variety of abstract design patterns that can result in confusing and hard to maintain code.