Wang et al., 2024) is a widely used technique for customizing large language models (LLMs) to handle

Wang et al., 2024) is a widely used technique for customizing large language models (LLMs) to handle

Large language models (LLMs) typically utilize the top-k contexts from a retriever in retrieval-augmented generation (RAG). In this work, we propose a novel instruction fine-tuning framework RankRAG, which instruction-tunes a single LLM for the dual purpose of context ranking and answer generation in RAG. In particular, the instruction-tuned LLMs work surprisingly well by adding a small fraction of ranking data into the training blend, and outperform existing expert ranking models, including the same LLM exclusively fine-tuned on a large amount of ranking data. For generation, we compare our model with many strong baselines, including GPT-4-0613, GPT-4-turbo-2024-0409, and ChatQA-1.5, an open-sourced model with the state-of-the-art performance on RAG benchmarks. Specifically, our Llama3-RankRAG significantly outperforms Llama3-ChatQA-1.5 and GPT-4 models on nine knowledge-intensive benchmarks. In addition, it also performs comparably to GPT-4 on five RAG benchmarks in the biomedical domain without instruction fine-tuning on biomedical data, demonstrating its superb capability for generalization to new domains.

Large language models (LLMs) are initially pretrained for broad capabilities and then finetuned with instruction-following datasets to improve their performance in interacting with humans. Despite advances in finetuning, a standardized guideline for selecting high-quality datasets to optimize this process remains elusive. In this paper, we first propose InstructMining, an innovative method designed for automatically selecting premium instruction-following data for finetuning LLMs. Specifically, InstructMining utilizes natural language indicators as a measure of data quality, applying them to evaluate unseen datasets. During experimentation, we discover that double descent phenomenon exists in large language model finetuning. Based on this observation, we further leverage BlendSearch to help find the best subset among the entire dataset (i.e., 2,532 out of 100,000). Experiment results show that InstructMining-7B achieves state-of-the-art performance on two of the most popular benchmarks: LLM-as-a-judge and Huggingface OpenLLM leaderboard.

General Motors is studying the possibility of an artificial intelligence voice assistant in future vehicles, according to the company. GM Chair and CEO Mary Barra, who was asked for details Tuesday by Fox Business channel anchor Liz Claman, referenced the company's Ultifi "end-to-end" vehicle software platform. "It's one of many things we can put on the vehicle. The vehicle really is a software platform and starting in 2019, General Motors started rolling out vehicles where you could do over-the-air updates for almost every module in the vehicle," Barra said, in an interview that touched on artificial intelligence, self-driving vehicles and a current production shutdown tied to supply chain issues at one of GM's truck plants. "Having an assistant with a voice that's clear enough where you can ask questions and get answers, I think that's what the artificial intelligence will enable us to do," Barra said, noting that "we'll be able to make your car better as you own it."

Hey, S voice, where can I get some free food nearby?

Cerence is offering innovative artificial technology for the automotive industry. Read what this means for CRNC stock.

This past year I hired, fired and then rehired an artificial intelligence scheduler called Amy. This is my first experience managing a machine. Sure, there was a novelty factor that was fun but she's also had a few West World episodes that were less fun. With so much media focus on how artificial intelligence and machines are replacing human knowledge workers, there is both excitementand fearabout the future of work. As intelligent technology can now interpret text, understand speech and make sense of images it has become possible to automate tasks that were previously the preserve of humans.

Facebook has teamed up with researchers at the Massachusetts Institute of Technology (MIT) to develop an artificial intelligence (AI) assistant for the world's most popular video game, Minecraft. This isn't an AI that will help you automatically build worlds, but rather one capable of multitasking and helping users with everyday tasks outside a gaming environment. Minecraft was chosen by the researchers because it's currently the most popular game in the world with more than 90-million people playing it every month and it has "infinite variety" yet simple predictable rules. Plus, there's a big opportunity for the AI assistant to learn inside'Minecraft' and help human players to acquire more knowledge outside the game. "The opportunities for an AI to learn are huge, Facebook is setting itself the task of designing the AI to self-improve, the researchers think the'Minecraft' environment is a perfect one to develop this kind of learning," said the report.

Artificial Intelligence is all the rage in today's'technologic' world where researchers are trying to find more applications for human benefit. Of course, AI is faster and has better problem solving capabilities compared to human beings but it lacks multitasking skills. SEE ALSO: Meet Astro, Boston Dynamics' Dog-Inspired Quadruped Robot Researchers at Facebook are using an interesting method to develop AI that will be good at multitasking and not just a single task. As reported by MIT Technology Review, people at Facebook Research are using Minecraft to train AI that can interact with humans and perform a plethora of tasks as requested. Minecraft is an open world sandbox video game where a seemingly infinite world with different biomes and mobs within which players can build, craft and survive with various online game modes designed by players and create beautiful'blocky' structures from statues to cities. SEE ALSO: Researchers Released DIY Instructions To Make A Wormhole!