Intersection Between LLMs and Products
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There are three important mindsets to consider when building LLMs (Large Language Models) products: rooting the problem, acting as a user, and having a growth mindset.
When applying these mindsets to LLMs, we should ask ourselves questions about whether an LLM is the right solution and how to handle potential biases in the data.
There are challenges associated with evaluating LLMs, such as a poor correlation between standard evaluation metrics and human judgment.
Some of the ethical considerations when deploying LLMs include hallucinations and ensuring users are not misled. We can mitigate these challenges by controlling the data used to train the model, implementing source citation, and focusing on controlled hallucinations.
Future LLMs are likely to focus on being task-specific, non-English languages, and having lower resource requirements. There are also considerations of the environmental cost of training large models.
Ultimately, LLMs should be a tool to solve real problems and should not be seen as a replacement for human judgment or expertise.