Parag Mahajani

Existing evaluation methods generally fall into two camps. The first relies on global similarity metrics, which can detect broad correlations with incompleteness but fail to pinpoint where content is missing. The second group uses supervised QA, natural language inference (NLI), or LLM-as-judge methods. These approaches provide more detailed adequacy signals but come with high costs in data, annotation and ongoing maintenance.

At the core of LLMs is their ability to predict the next correct word (or token) to their previously generated output in response to the input provided. The input content gets converted into tokens and then into embeddings; at the output layer, the embeddings get reconverted into tokens. The challenge, for the model, lies in the process of identifying the right token as an output for a given input. This process is statistical and stochastic in nature. It’s called sampling.

In other words, LLMs are insufficient in the pursuit of AGI. Of course, whether AGI is desirable or, indeed, if it’s possible at all, are other debates to be had, but what matters is that current interest in LLMs certainly won’t be the start of a path towards anything resembling AGI. Alternatives to transformer-based architectures Transformer-based architectures dominate the AI landscape.