Large Language Models for Product Managers: 5 Things to Know

The concept of "emergence" refers to the phenomenon where new abilities or properties emerge as a result of increasing the number of parameters in a model. In the context of Large Language Models (LLMs), this means that as the number of parameters increases, the LLM may acquire new skills or capabilities that were not evident when it had fewer parameters. This is because larger models are able to capture more complex patterns and relationships within the data they're trained on. These newly acquired abilities can span various tasks such as translation between languages, writing programming code, summarizing text, and others. Notably, LLMs acquire these skills through observation of recurring patterns in natural language during training, without explicit task-specific supervision. However, the phenomenon of emergence is not limited to LLMs and has been observed in other scientific contexts. For a more general discussion, readers can refer to "Emergent Abilities of Large Language Models". Surprisingly, these emergent abilities are sometimes accessible through well-crafted prompts: an LLM can perform certain tasks simply by receiving the appropriate query in natural language. For example, it can generate a concise summary when prompted with a passage followed by a summarization request. However, pre-trained LLMs may not always follow prompts accurately, possibly due to replicating patterns observed in training data. To overcome this, researchers developed Instruction Tuning, a strategy that trains LLMs on a small dataset of prompts or instructions followed by correct actions. Fine-tuning the model on these examples helps it better understand and follow natural language instructions. The main advantage of Instruction Tuning is the LLM's generalization capability, enabling it to follow instructions for a variety of tasks beyond those seen in the small dataset. This has partly replaced the need for extensive fine-tuning of smaller, specialized models for certain tasks, as large, scaled models can effectively perform them after exposure to diverse data and simple instruction tuning. LLMs can be prompted to perform tasks, which previously required fine-tuning a model through supervised learning. Despite these advances, there are still challenges associated with LLMs. One major concern is the potential for harmful or biased content generation due to exposure to large amounts of data on the internet during training. As general-purpose chatbots become increasingly popular, ensuring these models are not exploited for malicious purposes becomes crucial. Several strategies such as Reinforcement Learning from Human Feedback (RLHF) have been developed to align LLMs with human values and reduce the likelihood of harmful responses. However, addressing all potential risks associated with LLM use remains an ongoing challenge in AI safety research.