Open-Source Models, Temperature Scaling, Re-Ranking, and More: Don’t Miss Our Recent LLM Must-Reads | by TDS Editors | May, 2024

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New LLMs continue to arrive on the scene almost daily, and the tools and workflows they make possible proliferate even more quickly. We figured it was a good moment to take stock of some recent conversations on this ever-shifting terrain, and couldn’t think of a better way to do that than by highlighting some of our strongest articles from the past couple of weeks.

The lineup of posts we put together tackle high-level questions and nitty-gritty problems, so whether you’re interested in AI ethics, the evolution of open-source technology, or innovative RAG approaches, we’re certain you’ll find something here to pique your interest. Let’s dive in.

• Shifting Tides: The Competitive Edge of Open Source LLMs over Closed Source LLMs
  The initial wave of generative-AI tools was spearheaded by proprietary models like the ones released by OpenAI. Leonie Monigatti’s new article focuses on an emerging trend: the rise—and increasing dominance—of smaller open-source foundation models that are making a splash thanks to factors like data security, customizability, and cost.
• Chatbot Morality?
  We know LLMs can produce hallucinations when asked for factual information; what happens when users start prompting them for ethics-focused advice? Eyal Aharoni and Eddy Nahmias present their latest research on this thorny question and the dangers inherent to the perception of morality in chatbots that “can imitate or synthesize human moral discourse in specific, controlled circumstances.”
• Can Recommendations from LLMs Be Manipulated to Enhance a Product’s Visibility?
  E-commerce is an area that’s already susceptible to manipulation and questionable business practices. As Parul Pandey shows in her analysis of a recent paper, LLMs—with their power to produce text and other media rapidly and at scale—are already primed to exploit various loopholes and blind spots in this ecosystem.