Chapter 2: Questions & Answers

Text models currently struggle to produce factually correct responses when asked questions about factual information. They can generate responses that appear compelling to laymen but are entirely incorrect. This problem is attributed to the current challenges in natural language processing which include contextual words, homonyms, synonyms, sarcasm, and ambiguity. The negative societal implications of text generation are fake news and the spread of disinformation. It can be used to produce believable content on a massive scale with much greater efficiency and lower barriers to entry. It can also be used for harmful activities like spam, phishing, abuse of legal and government processes, fraudulent academic writing, and pretexting.