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
Industry challenges
Open challenges in LLM research
Never before in my life had I seen so many smart people working on the same goal: making LLMs better. After talking to many people working in both industry and academia, I noticed the 10 major research directions that emerged. The first two directions, hallucinations and context learning, are probably the most talked about today. I’m the most excited about numbers 3 (multimodality), 5 (new architecture), and 6 (GPU alternatives).
https://huyenchip.com/2023/08/16/llm-research-open-challenges.html
Emergent ability
Emergent Abilities of Large Language Models
Emergence can be defined as the sudden appearance of novel behavior. Large Language Models apparently display emergence by suddenly gaining new abilities as they grow. Why does this happen, and what does this mean?
https://www.assemblyai.com/blog/emergent-abilities-of-large-language-models
Catching up on the weird world of LLMs
I gave a talk on Sunday at North Bay Python where I attempted to summarize the last few years of development in the space of LLMs—Large Language Models, the technology …
https://simonwillison.net/2023/Aug/3/weird-world-of-llms
