Large language models (LLMs) with residual augmented-generation (RAG) have
been the optimal choice for scalable generative AI solutions in the recent
past. However, the choice of use-cases that incorporate RAG with LLMs have been
either generic or extremely domain specific, thereby questioning the
scalability and generalizability of RAG-LLM approaches. In this work, we
propose a unique LLM-based system where multiple LLMs can be invoked to enable
data authentication, user query routing, data retrieval and custom prompting
for question answering capabilities from data tables that are highly varying
and large in size. Our system is tuned to extract information from
Enterprise-level data products and furnish real time responses under 10
seconds. One prompt manages user-to-data authentication followed by three
prompts to route, fetch data and generate a customizable prompt natural
language responses. Additionally, we propose a five metric scoring module that
detects and reports hallucinations in the LLM responses. Our proposed system
and scoring metrics achieve >90% confidence scores across hundreds of user
queries in the sustainability, financial health and social media domains.
Extensions to the proposed extreme RAG architectures can enable heterogeneous
source querying using LLMs.

我们提出了一种基于大型语言模型的系统，可以通过多个大型语言模型从各种规模大的企业级数据产品中提取信息，并在 10 秒内提供实时响应，同时提出了一个得分模块来检测和报告大型语言模型响应中的幻觉。在可持续发展、财务健康和社交媒体领域的数百个用户查询中，我们的系统和评分指标实现了超过 90% 的置信分数。

ERATTA：基于大型语言模型的表格问答极端阅读简化

ERATTA: Extreme RAG for Table To Answers with Large Language Models

Autonomous driving systems are increasingly popular in today's technological
landscape, where vehicles with partial automation have already been widely
available on the market, and the full automation era with ``driverless''
capabilities is near the horizon. However, accurately understanding humans'
commands, particularly for autonomous vehicles that have only passengers
instead of drivers, and achieving a high level of personalization remain
challenging tasks in the development of autonomous driving systems. In this
paper, we introduce a Large Language Model (LLM)-based framework Talk-to-Drive
(Talk2Drive) to process verbal commands from humans and make autonomous driving
decisions with contextual information, satisfying their personalized
preferences for safety, efficiency, and comfort. First, a speech recognition
module is developed for Talk2Drive to interpret verbal inputs from humans to
textual instructions, which are then sent to LLMs for reasoning. Then,
appropriate commands for the Electrical Control Unit (ECU) are generated,
achieving a 100\% success rate in executing codes. Real-world experiments show
that our framework can substantially reduce the takeover rate for a diverse
range of drivers by up to 90.1\%. To the best of our knowledge, Talk2Drive
marks the first instance of employing an LLM-based system in a real-world
autonomous driving environment.

利用大型语言模型（LLM）的 Talk-to-Drive 框架，通过处理人类的口头指令并结合上下文信息做出自主驾驶决策，实现个性化的安全、高效和舒适，成功率达到 100% 的执行命令，并在现实世界的实验中将不同驾驶员的接管率大幅降低至高达 90.1%，是在真实自主驾驶环境中应用 LLM 的首次实例。