Edition 30 - Should You Trust an LLM to Pick Stocks?
The Evaluator is a collection of top content we've published recently at Arize AI. In this month's edition we look at how well LLMs can detect anomalous time series patterns, break down LLM summarization, and tell you everything you need to know about running and benchmarking evals.
As always, we conclude with a list of some of our favorite news, papers, and community threads.
Read on and dive in...
LLM Performance At Time Series Analysis: GPT-4 versus Claude
Given a large set of time series data within the context window, how well can LLMs detect anomalies or movements in the data? In other words, should you trust your money with a stock-picking GPT-4 or Claude 3 agent?
Aparna Dhinakaran and Evan Jolley set out to investigate these questions by conducting a series of experiments comparing the performance of large language models in detecting anomalous time series patterns. Read it.
Arize AI Brings LLM Evaluation, Observability To Microsoft Azure AI Model Catalog
Generative AI is reshaping the modern enterprise. According to a recent survey, over half (61%) of developers say they plan to deploy LLM applications into production in the next 12 months or “as soon as possible.”
Jason Lopatecki explains that challenges remain in getting a generative application from toy to production – and staying there. At Microsoft Build, Arize AI announced an integration with Azure AI Model as a Service to help AI engineers speed the reliable deployment of LLM applications. Read it.
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LLM Summarization: Getting to Production
This article by Olumide Shittu dives into the concept of LLM summarization – why it is important, primary summarization approaches and challenges, and a code-along example of LLM summarization evaluation using Arize Phoenix. Read it.
LLM Evaluation: Everything You Need To Run, Benchmark LLM Evals
LLMs are an incredible tool for developers and business leaders to create new value for consumers. They make personal recommendations, translate between structured and unstructured data, summarize large amounts of information, and do so much more.
As Aparna Dhinakaran explains (with Ilya Reznik), as the applications multiply, so does the importance of measuring the performance of LLM-powered applications. Read it.
Meet us July 11 in SF at Arize:Observe
We’re gearing up for Arize:Observe–the year’s premier LLM evaluation and observability event.?Meet major model creators, open source tool builders, and researchers for one day of pioneering and learning together in the heart of the action SHACK15 in San Francisco. Register now.
Staff picks ??
Here's a roundup of our team's favorite news, papers, and community threads recently.