Why editing the knowledge of LLMs post-training can create messy ripple effects
After the advent of ChatGPT, the readily available model developed by Open AI, large language models (LLMs) have become increasingly widespread, with many online users now accessing them daily to quickly get answers to their queries, source information or produce customized texts. Despite their striking ability to rapidly define words and generate written texts pertinent to a user’s queries, the answers given by these models are not always accurate and reliable.
Large language models (LLMs), such as the GPT-4 model underpinning the widely used conversational platform ChatGPT, have surprised users with their ability to understand written prompts and generate suitable responses in various languages. Some of us may thus wonder: are the texts and answers generated by these models so realistic…
Psychologists and behavioral scientists have been trying to understand how people mentally represent, encode and process letters, words and sentences for decades. The introduction of large language models (LLMs) such as ChatGPT, has opened new possibilities for research in this area, as these models are specifically designed to process and…