Language models are achieving impressive performance on various tasks by aggressively adopting inference-time prompting techniques, such as zero-shot and few-shot prompting. In this work, we introduce EchoPrompt, a simple yet effective approach that prompts the model to rephrase its queries before answering them. EchoPrompt is adapted for both zero-shot and few-shot in-context learning with standard and chain-of-thought prompting. Experimental results show that EchoPrompt yields substantial improvements across all these settings for four families of causal language models. These improvements are observed across various numerical reasoning (e.g. GSM8K, SVAMP), reading comprehension (e.g. DROP), and logical reasoning (e.g. Coin Flipping) tasks. On average, EchoPrompt improves the Zero-shot-CoT performance of code-davinci-002 by 5% in numerical tasks and 13% in reading comprehension tasks. We investigate the factors contributing to EchoPrompt's effectiveness through ablation studies, which reveal that both the original query and the model-generated rephrased version are instrumental in its performance gains. Our empirical results indicate that EchoPrompt is an effective technique that enhances in-context learning performance. We recommend incorporating EchoPrompt into various baseline prompting strategies to achieve performance boosts.

Randomly masking text spans in ordinary texts in the pre-training stage hardly allows models to acquire the ability to generate simple texts. It can hurt the performance of pre-trained models on text simplification tasks. In this paper, we propose a new continued pre-training strategy to teach the pre-trained model to generate simple texts. We continue pre-training BART, a representative model, to obtain SimpleBART. It consistently and significantly improves the results on lexical simplification, sentence simplification, and document-level simplification tasks over BART. At the end, we compare SimpleBART with several representative large language models (LLMs).

Machine learning is the subfield of computer science, that provides computers the ability to automatically learn on their own and improve from their experiences without being explicitly programmed. Though it has been hidden in the recent past but still machine learning has become a basic pillar of IT. We are constantly being surrounded by several ML-based applications like search engines, anti-spam filters, credit card fraud detection system, etc etc. ML is the subset of Artificial Intelligence, which deals with structured and semi-structured data. To understand the concept behind ML, a precise overview of AI is necessary. When AI was coined first in 1955, it's aim was to make machines that are able to perform unique human-based tasks that require intelligence.

"We cannot solve our problems with the same thinking we used when we created them." GitHub just recently introduced their AI tool called GitHub Copilot which helps software developers write code. But a lot of people don't know what the GitHub Copilot is, some aren't even aware it exists. That's what this article is for, to tell you all about the GitHub Copilot. GitHub Copilot is an AI pair programmer that helps developers write better code by giving suggestions based on the code being currently worked on.

A few years back, it was difficult to imagine a world in which we can interact with digital humans on a daily basis. But as of today, you can encounter a digital human as a part of a layered digital fabric integrated into a website's customer support bot or in a game you are playing. You will only realize the intensity of the artificial human interaction if you are unable to distinguish the bot from a real human. If any of these three types compete for the level of an actual human, then that is because of a great execution of an ultra-high-quality, real-time rendered model that would bring life to the data. The approach for creating such kind of bot can be a lot of manual and scripted animation (incl motion capture) and months of character artistry Or it can be a single Deep Learning model, that does the same thing in a fraction of a second.

Businesses today are drawing close to the digital transformation, guiding them to improve customer service and stay in the competition. Like from Artificial Intelligence (AI) to IoT (Internet of Thing), these technologies play a crucial role in improving business performance. Businesses stay connected and communicate with visitors visiting sites. One of the best ways to communicate and engage with visitors is using Chatbots. When we talk about the technologies, chatbots are now the center of business communication or messaging.

We feed the test image to the trained model, compares the predicted output with test image's label to evaluate either it's correct or wrong prediction. At the end, we will have the count of correct matches and the incorrect matches. The key realization we need to make, is that in the real world not all incorrect and correct matches hold equal value. Also in the real world, a single metric won't tell the complete story, that's why previously mentioned four metrics are used to evaluate the model. We could organize our predicted values compared to the real values in a confusion matrix.

Machine Learning and Data Science -- these are the phrases that are always seen to be used in consonance. Both are modern viral technologies that are advancing at a rapid rate today and are interdependent. This article will address Machine Learning, the interdependency of Data Science and Machine Learning, their differences, and their importance. Machine Learning is a method used in data analysis that systematizes model building and extracts knowledge from data. It is also known as Predictive Analysis or Statistical Learning, a branch of Artificial Intelligence built on the concept of systems learning and observing patterns from existing Data with minimum or zero human intervention.

Python, you've heard of it and wonder what's so special with this language. With the rise of Machine Learning and Artificial Intelligence, it is impossible to get away from it. You may question yourself, is Python easy to learn? Let me tell you, it actually is! and I am here to help you get started with Python basics. Python in simple words is a High-Level Dynamic Programming Language which is interpreted. Guido van Rossum, the father of Python had simple goals in mind when he was developing it, easy looking code, readable and open source. Python is ranked as the 3rd most prominent language followed by JavaScript and Java in a survey held in 2018 by Stack Overflow which serves proof to it being the most growing language. Python is currently my favorite and most preferred language to work on because of its simplicity, powerful libraries, and readability. You may be an old school coder or may be completely new to programming, Python is the best way to get started! To sum it up, Python has a simple syntax, is readable, and has great community support.

Starting learning a new field is always not easy. The best way to learn is to start from the basics because it will make the ground for all your further knowledge. First of all, let's understand what all those buzzwords actually mean. The difficulty in understanding those terms is that they are overlapping and some people mistakenly think that they are also interchangeable, and they are not, of course. But first sings first, let's define what those terms mean one by one.