Seonglae ChoLarge Language Models (Brown et al., 2020; OpenAI, 2023)
Think LLMs as simulators but as entities. "What do you think about xyz"? There is no "you". Next time try: "What would be a good group of people to explore xyz? What would they say?"‣.
Current transformer-based LLMs clearly possess a usable level of intelligence, but they don't have identity like humans do. Due to the vast amount of training data, numerous intelligences or identities are overlapped, which is why inconsistent results occur. This is actually an advantage from a simulation perspective - when conversing with an LLM, if you think of it as an entity, you might get angry or emotional, but if you view it as prompt simulation, you won't.
Commodity infrastructure for service implementation
Industry challenges
LLM Notion
Large Language Models
LLM Usages
