Consider a multi-class labelling problem, where the labels can take values in [k], and a predictor predicts a distribution over the labels. In this work, we study the following foundational question: Are there notions of multi-class calibration that give strong guarantees of meaningful predictions and… Read More »On Computationally Efficient Multi-Class Calibration Apple Machine Learning Research
Self-attention and masked self-attention are at the heart of Transformers’ outstanding success. Still, our mathematical understanding of attention, in particular of its Lipschitz properties — which are key when it comes to analyzing robustness and expressive power — is incomplete. We provide a detailed study… Read More »How Smooth Is Attention? Apple Machine Learning Research
Despite the successes of large language models (LLMs), they exhibit significant drawbacks, particularly when processing long contexts. Their inference cost scales quadratically with respect to sequence length, making it expensive for deployment in some real-world text processing applications, such as retrieval-augmented generation (RAG). Additionally, LLMs… Read More »Superposition Prompting: Improving and Accelerating Retrieval-Augmented Generation Apple Machine Learning Research
Beyond minimizing a single training loss, many deep learning estimation pipelines rely on an auxiliary objective to quantify and encourage desirable properties of the model (e.g. performance on another dataset, robustness, agreement with a prior). Although the simplest approach to incorporating an auxiliary loss is… Read More »Careful With That Scalpel: Improving Gradient Surgery With an EMA Apple Machine Learning Research
It’s no exaggeration to say that chatbots are a mainstream technology. A whopping 88% of web users chatted with bots in the previous year. What’s more, 7 out of 10 find the experience positive. However, despite these promising statistics, there’s still a significant portion… Read More »Is Your Chatbot Sabotaging Your Business? The Tell-Tale Signs and a Rescue Plan Master Of Code Global Chatbots Life – Medium
Natural Language Processing (NLP) is a fascinating field that bridges the gap between human communication and machine understanding. One of the fundamental steps in NLP is text preprocessing, which transforms raw text data into a format that can be effectively analyzed and utilized by… Read More »Exploring NLP Preprocessing Techniques: Stopwords, Bag of Words, and Word Cloud Ravjot Singh Becoming Human: Artificial Intelligence Magazine – Medium
[[{“value”:” Personalized review generation within recommender systems is an area of increasing interest, particularly in creating custom reviews based on users’ historical interactions and preferences. This involves utilizing data about users’ previous purchases and feedback to produce reviews that accurately reflect their unique preferences and… Read More »Review-LLM: A Comprehensive AI Framework for Personalized Review Generation Using Large Language Models and User Historical Data in Recommender Systems Asif Razzaq Artificial Intelligence Category – MarkTechPost
[[{“value”:” Language modeling has significantly progressed in developing algorithms to understand, generate, and manipulate human language. These advancements have led to large language models that can perform translation, summarization, and question-answering tasks. These models are crucial for natural language processing (NLP) and artificial intelligence (AI)… Read More »Researchers from Stanford and the University at Buffalo Introduce Innovative AI Methods to Enhance Recall Quality in Recurrent Language Models with JRT-Prompt and JRT-RNN Asif Razzaq Artificial Intelligence Category – MarkTechPost
[[{“value”:” In the diverse toolkit available for deploying cloud infrastructure, Agents for Amazon Bedrock offers a practical and innovative option for teams looking to enhance their infrastructure as code (IaC) processes. Agents for Amazon Bedrock automates the prompt engineering and orchestration of user-requested tasks. After… Read More »Using Agents for Amazon Bedrock to interactively generate infrastructure as code Akhil Raj Yallamelli AWS Machine Learning Blog
[[{“value”:” Retrieval Augmented Generation (RAG) is a popular paradigm that provides additional knowledge to large language models (LLMs) from an external source of data that wasn’t present in their training corpus. RAG provides additional knowledge to the LLM through its input prompt space and its… Read More »Improve RAG accuracy with fine-tuned embedding models on Amazon SageMaker Ennio Pastore AWS Machine Learning Blog