![]() It’s no secret that large language models can spew out false-even hateful-text, but researchers have found that fixing the problem is not on the to-do list of most Big Tech firms. While OpenAI was wrestling with GPT-3’s biases, the rest of the tech world was facing a high-profile reckoning over the failure to curb toxic tendencies in AI. As OpenAI acknowledged: “Internet-trained models have internet-scale biases.” December 2020: Toxic text and other problems GPT-3 soaked up much of the disinformation and prejudice it found online and reproduced it on demand. It was also trained on a lot more data.īut training on text taken from the internet brings new problems. GPT-3 has 175 billion parameters (the values in a network that get adjusted during training), compared with GPT-2’s 1.5 billion. One of the most remarkable takeaways is that GPT-3’s gains came from supersizing existing techniques rather than inventing new ones. GPT-3 can answer questions, summarize documents, generate stories in different styles, translate between English, French, Spanish, and Japanese, and more. Its ability to generate human-like text was a big leap forward. GPT-2 was impressive, but OpenAI’s follow-up, GPT-3, made jaws drop. ![]() OpenAI claimed to be so concerned people would use GPT-2 “to generate deceptive, biased, or abusive language” that it would not be releasing the full model. Many previous successes in machine-learning had relied on supervised learning and annotated data, but labeling data by hand is slow work and thus limits the size of the data sets available for training.īut it was GPT-2 that created the bigger buzz. ![]() This lets the software figure out patterns in the data by itself, without having to be told what it’s looking at. GPT combined transformers with unsupervised learning, a way to train machine-learning models on data (in this case, lots and lots of text) that hasn’t been annotated beforehand. GPT (short for Generative Pre-trained Transformer) planted a flag, beating state-of-the-art benchmarks for natural-language processing at the time. The company wants to develop multi-skilled, general-purpose AI and believes that large language models are a key step toward that goal. OpenAI’s first two large language models came just a few months apart. For example, “hot dog” means very different things in the sentences “A hot dog should be given lots of water” and “A hot dog should be eaten with mustard.” 2018–2019: GPT and GPT-2 By tracking this contextual information, transformers can handle longer strings of text and capture the meanings of words more accurately. The meaning of words often depends on the meaning of other words that come before or after. The breakthrough behind today’s generation of large language models came when a team of Google researchers invented transformers, a kind of neural network that can track where each word or phrase appears in a sequence. ![]()
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