LLM tagged posts

As LLMs Grow Bigger, they’re more likely to give Wrong Answers than Admit Ignorance

As LLMs grow bigger, they're more likely to give wrong answers than admit ignorance
Performance of a selection of GPT and LLaMA models with increasing difficulty. Credit: Nature (2024). DOI: 10.1038/s41586-024-07930-y

A team of AI researchers at Universitat Politècnica de València, in Spain, has found that as popular LLMs (Large Language Models) grow larger and more sophisticated, they become less likely to admit to a user that they do not know an answer.

In their study published in the journal Nature, the group tested the latest version of three of the most popular AI chatbots regarding their responses, accuracy, and how good users are at spotting wrong answers.

As LLMs have become mainstream, users have become accustomed to using them for writing papers, poems or songs and solving math problems and other tasks, and the issue of accuracy has become a bigger...

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Language Agents Help Large Language Models ‘Think’ Better and Cheaper

Language agents help large language models 'think' better and cheaper
An example of the agent producing task-specific instructions (highlighted) for a classification dataset IMDB. The agent only runs once to produce the instructions. Then, the instructions are used for all our models during reasoning. Credit: arXiv (2023). DOI: 10.48550/arxiv.2310.03710

The LLMs that have increasingly taken over the tech world are not “cheap” in many ways. The most prominent LLMs, such as GPT-4, took some $100 million to build in the form of legal costs of accessing training data, computational power costs for what could be billions or trillions of parameters, the energy and water needed to fuel computation, and the many coders developing the training algorithms that must run cycle after cycle so the machine will “learn.”

But, if a researcher needs to do a specializ...

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Generative AI brings us Closer to Automating Investment Expertise

Credit: CC0 Public Domain

Large language models (LLMs) such as ChatGPT and Google Gemini excel at being trained on large data-sets to generate informative responses to prompts. Yi Cao, an assistant professor of accounting at the Donald G. Costello College of Business at George Mason University, and Long Chen, associate professor and area chair of accounting at Costello, are actively exploring how individual investors can use LLMs to glean market insights from the dizzying array of available data about companies.

Their new working paper, appearing in SSRN Electronic Journal and co-authored with Jennifer Wu Tucker of the University of Florida and Chi Wan of University of Massachusetts Boston, examines AI’s ability to identify “peer firms,” or product market competitors in an industry.

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Engineers Recreate Star Trek’s Holodeck using ChatGPT and Video Game Assets

Penn Engineers recreate Star Trek's Holodeck using ChatGPT and video game assets
Essentially, Holodeck engages a large language model (LLM) in a conversation, building a virtual environment piece by piece. Credit: Yue Yang

In “Star Trek: The Next Generation,” Captain Picard and the crew of the U.S.S. Enterprise leverage the Holodeck, an empty room capable of generating 3D environments, of preparing for missions and entertaining them, simulating everything from lush jungles to the London of Sherlock Holmes.

Deeply immersive and fully interactive, Holodeck-created environments are infinitely customizable, using nothing but language; the crew has only to ask the computer to generate an environment, and that space appears in the Holodeck.

Today, virtual interactive environments are also used to train robots prior to real-world deployment in a process called “Sim2...

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