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Comparison of LLMs: Lies, Damned Lies, and Benchmarks 1/6




In the ever-expanding universe of Large Language Models (LLMs), one might be forgiven for feeling a bit like Alice tumbling down the rabbit hole. With each passing day, a new model emerges, boasting capabilities that would make Turing himself raise an eyebrow. But as we navigate this wonderland of artificial intelligence, we find ourselves asking: Are these claims as solid as a Cheshire Cat, or do they disappear upon closer inspection?

Welcome, dear reader, to the wild world of LLM comparisons, where benchmarks reign supreme and the quest for AI supremacy has tech giants and plucky startups alike engaged in a high-stakes game of “My model can beat up your model.” In this blog post, we’ll dive deep into the methods used to evaluate these silicon-based savants, explore the areas of application being tested, and attempt to separate the wheat from the chaff in a landscape where everyone claims to be the cream of the crop.

So, grab your favorite beverage, put on your critical thinking cap, and join us as we embark on a journey through the land of lies, damned lies, and benchmarks. Don’t worry; we promise it’ll be more fun than a barrel of chatbots!

The LLM Landscape: Major Players and Their Claims

Picture, if you will, a bustling marketplace where instead of fruit vendors shouting about their wares, you have AI researchers and tech companies proclaiming the virtues of their latest language models. “Step right up and witness the marvel of GPT-4o, now with 50% more common sense!” “Behold the wonder of PaLM 2, able to leap tall buildings and solve differential equations in a single bound!” “Don’t miss the incredible Claude, now with extra helpfulness and a dash of existential uncertainty!”

In this cacophony of claims, we find ourselves surrounded by an alphabet soup of model names: BERT, RoBERTa, T5, GPT-3, GPT-4o, LaMDA, PaLM, Chinchilla, BLOOM, OPT, and more. Each comes with its own set of impressive-sounding statistics and capabilities that would make any résumé blush.

OpenAI’s GPT series, particularly GPT-3 and GPT-4o, have been making waves with their ability to generate human-like text, engage in complex reasoning, and even attempt to write code. Not to be outdone, Google has thrown its considerable weight behind models like LaMDA and PaLM, touting their conversational prowess and multi-modal capabilities.

Meanwhile, companies like Anthropic, with their Claude model, emphasize ethical considerations and aim for more “honest” AI. And let’s not forget the open-source contenders like BLOOM and OPT, democratizing access to large language models while challenging the notion that bigger always means better.

But here’s the rub: with each company singing praises of their own creation, how can we, mere mortals, possibly compare these silicon savants? Enter the world of benchmarks, where models are put through their paces in a series of tests designed to measure everything from basic language understanding to advanced reasoning capabilities.

As we delve deeper into these benchmarks, keep in mind the words of the great philosopher Inigo Montoya: “You keep using that word. I do not think it means what you think it means.” In the world of LLM comparisons, impressive numbers don’t always translate to real-world performance, and the devil, as they say, is in the details.

In our next section, we’ll pull back the curtain on these benchmarking methods and examine exactly what’s being measured. Spoiler alert: It’s not always what you’d expect!