CybAI news

arXiv:2407.13781v1 Announce Type: new Abstract: Recently, various encoder-only and encoder-decoder pre-trained models like BERT and T5 have been applied to automatic essay scoring (AES) as small language models. However, existing studies have primarily treated this task akin to a classification problem, focusing solely on outputting scores in the target text without offering interpretations for the generated scores. Departing from the approaches, we introduce Reasoning Distillation-Based Evaluation (RDBE), which integrates interpretability to elucidate the rationale behind model scores while enhancing performance through initial reasoning. This interpretive capability is acquired during training by leveraging generated reasoning from a large language model (LLM)…

(Roman-Samborskyi/Shutterstock) Doesn’t it seem like there’s a new machine learning model introduced every week? That’s probably because there is. From Sora to LLaMA-3 and Claude 2, models today come in all shapes and sizes—open source, off the shelf—with varying performance rates, cost implications, and rate limits. Each provider makes big promises to revolutionize the industry, and your business in particular. But the reality is that model fatigue is setting in. Choosing a model today is like walking down the cereal aisle at the grocery store. We’re spoiled for choice, and choice is good. But unlike cereal, you can’t just throw…

In this article, we will be fine tuning the LRASPP MobileNetV3 model on the KITTI segmentation dataset. This article is the first in the series where we fine-tune the LRASPP MobileNetV3 model for Autonomous Driving Scene Segmentation. For this, we will start with fine-tuning the model on the KITTI semantic segmentation dataset. This is a small dataset that will kickstart our experiments. We will try two different training approaches and figure out how well the model can segment complex driving scenes. In the next two articles, we will use larger segmentation datasets for fine tuning to produce better models and…