How to Build an LLM Evaluation Framework, from Scratch
A Guide to Build Your Own Large Language Models from Scratch by Nitin Kushwaha Some of the common preprocessing steps include removing HTML Code, fixing spelling mistakes, eliminating toxic/biased data, converting emoji into their text equivalent, and data deduplication. Data deduplication is one of the most significant preprocessing steps while training LLMs. Data deduplication refers to the process of removing duplicate content from the training corpus. The need for LLMs arises from the desire to enhance language understanding and generation capabilities...