In this supplementary material, we first describe the companion video that provides an overview of our dataset (Section 1) and then detail how the data was released (Section 2) along with taking stock of additional information specifically promised in the checklist (Section 3). Next, we provide additional details on the dataset construction (Section 4) and on the benchmarks (Section 5). We devote a final section (Section 6) to showing that the EPIC Fields pipeline could be applied to reconstructing videos from the Ego4D dataset. We provide a short video in the form of a trailer at https://youtu.be/RcacE26eObE. It allows to visually assess how challenging the reconstruction problem is and hints at how frame filtering helps. The video also illustrates how the new camera poses complement the existing semantic annotations for this dataset (hands and active objects), showcasing the potential of marrying 3D geometry and video understanding.

Lexical Simplification (LS) methods use a three-step pipeline: complex word identification, substitute generation, and substitute ranking, each with separate evaluation datasets. We found large language models (LLMs) can simplify sentences directly with a single prompt, bypassing the traditional pipeline. However, existing LS datasets are not suitable for evaluating these LLM-generated simplified sentences, as they focus on providing substitutes for single complex words without identifying all complex words in a sentence. To address this gap, we propose a new annotation method for constructing an all-in-one LS dataset through human-machine collaboration. Automated methods generate a pool of potential substitutes, which human annotators then assess, suggesting additional alternatives as needed. Additionally, we explore LLM-based methods with single prompts, in-context learning, and chain-of-thought techniques. We introduce a multi-LLMs collaboration approach to simulate each step of the LS task. Experimental results demonstrate that LS based on multi-LLMs approaches significantly outperforms existing baselines.

In this contributed article, Taggart Bonham, Product Manager of Global AI at F5 Networks, discusses last June, OpenAI released GPT-3, their newest text-generating AI model. As seen in the deluge of Twitter demos, GPT-3 works so well that people have generated text-based DevOps pipelines, complex SQL queries, Figma designs, and even code. In the article, Taggart explains how enterprises need to prepare for the AI economy by standardizing their data collection processes across their organizations like GPT-3 so it can then be properly leveraged.