Research focussed on the evaluation of AI models in general and LLMs in specific.
This paper serves as a comprehensive overview of benchmark studies…The study also distills the process of evaluating LLMs into three main categories; the what, the where and the how. These principles are also important when designing a LLM-based application.
LLMs to produce more coherent and contextually relevant responses, rendering them highly suitable for interactive and conversational applications.
Reinforcement Learning from Human Feedback stands as another pivotal aspect of LLMs. This technique involves refining the model by using human-generated responses as rewards, enabling the model to learn from its mistakes and enhance its performance progressively.
In the context of the table presented below, the comparison of traditional Machine Learning, Deep Learning, and LLMs across six essential elements is very insightful.
As seen in the LLM column, interpretability is a challenge with LLMs with high model complexity.
As depicted in the image blow, the study explores LLM evaluation in three dimensions and challenges can be considered as a fourth.
Not only do these three dimensions serve as integral components to the evaluation of LLMs, it also serves as a reference for conceptualising and planning LLM-based products.
Adapted From Source
Final Thoughts
There is not one large language model to rule them all, it seems evident that model orchestration will become increasingly important.
The study considers the current ecosystem to discover new trends and protocols and propose new challenges and opportunities.
Find the original research here.
