GPT-3 has Set a Benchmark, But It’s Not What You Think

When GPT-3 came online, it was the talk of the town, and it still is. The reason is primarily the huge dataset the large language model (LLM) was trained on. Many upcoming LLMs consider GPT-3 as a benchmark while launching their product; for instance, recently, MosaicML put out a blog titled, ‘Mosaic LLMs (Part 2): GPT-3 quality for <$500k’.  But is the good old LLM really the benchmark?  It can’t be possible for an LLM model like GPT-3, which is based on a huge dataset, to be the benchmark for the upcoming language learning models. The model is not even showing better performance in terms of truthfulness when compared to the earlier LLMs like GPT-Neo/GPT-J, GPT-2, and T5-based models.  For example, if we compare the percentage of truth from various GPT-3 mode