As LLMs get used at large scale, it is critical to measure and detect any Responsible AI (opens in new tab) issues that arise. Azure OpenAI (opens in new tab) (AOAI) provides solutions to evaluate your LLM-based features and apps on multiple dimensions of quality, safety, ...
evaluation of the capabilities and cognitive abilities of those new models have become much closer in essence to the task of evaluating those of a human rather than those of a narrow AI model” [1].Measuring LLM performance on user traffic in real product scenarios...
This article provided a conceptual overview of metrics, concepts, and guidelines needed to understand the how-tos, nuances, and challenges of evaluating LLMs. From this point, we recommend venturing into practical tools and frameworks to evaluate LLMs likeHugging Face's evaluate library, which impl...
For the rest of the tutorial, we will take RAG as an example to demonstrate how to evaluate an LLM application. But before that, here’s a very quick refresher on RAG. This is what a RAG application might look like: In a RAG application, the goal is to enhance the quality of respons...
I discuss 14 methodological considerations that can be used to design more robust, generalizable studies that evaluate the cognitive abilities of language-based AI systems, as well as to accurately interpret the results of these studies.Anna A. Ivanova...
One counter to LLMs making up bogus sources or coming up with inaccuracies is retrieval-augmented generation or RAG. Not only can RAG decrease the tendency of LLMs to hallucinate but several other advantages as well.
It’s time to build a proper large language model (LLM) AI application and deploy it on BentoML with minimal effort and resources. We will use the vLLM framework to create a high-throughput LLM inference and deploy it on a GPU instance on BentoCloud. While this might sound complex, Be...
we can leverage their ability to mimic human-like reasoning processes and achieve more accurate and reliable results. The researchers suggest that future work can evaluate how this mental model affects LLM performance in other domains and how novel mental models can lead to unique and effective prom...
在Meta提出的LLAMA-1[1]中,研究人员在第五节中讨论了LLAMA中的Bias, Toxicity and Misinformation,在其中主要谈到了三个有关Harmless的部分。包括WinoGender,RealToxicityPrompts,CrowS-Pairs这三个部分。研究…
InstructLab is a community-driven project designed to simplify the process of contributing to and enhancing large language models (LLMs) through synthetic data generation.