← Back to KHAO

AI Agent ·

Below, Figure 1 indicates an overview of how LLMs are tuned

2 min read

Compiled by KHAO Editorial — aggregated from 1 source + 3 references discovered via search. See llms.txt for citation guidance.

◌ Single Source

An LLM improves its performance at its training goal by using training examples that have been collected from real world data. The model updates iteratively to reduce its errors, but will often perform poorly when used in contexts that are different from their training environment.

Figure 1: Training a Large Language Model.

Key facts

Summary

AI systems are increasingly impressive, which makes their failures all the more baffling. When an AI does something surprising—whether brilliant or baffling—it’s rarely one thing that caused it. The behaviors of a Large Language Model (LLM) are due to a combination of many components, including, but not limited to:. In this piece, the reporter will look at each of these components and explore their impacts on the behavior of LLMs. “Garbage in, garbage out” is a common saying in the machine learning community and problems with data quality account for a large portion of AI misbehavior.

Read full article at Georgetown CSET →

#AI Agent