A recent summary on language model finetuning highlights the various approaches and goals of fine-tuning techniques. Fine-tuning refers to further training a pre-trained language model, with two primary goals: learning new knowledge or aligning the output format to a specific style or structure.

The article discusses large-scale instruction tuning, which involves training models like FLAN on massive datasets. This approach allows for efficient learning of multiple downstream tasks. Another goal is alignment-focused fine-tuning, which aims to adapt the language model's style without requiring extensive data. Research shows that most LLMs' knowledge comes from pretraining, and alignment teaches the correct style.

The article also explores imitation models, which aim to mimic proprietary LLMS by fine-tuning smaller models on smaller datasets. However, these models failed to accurately assess their performance, leading to misleading claims.

Source: https://stackoverflow.blog/2025/10/31/a-brief-summary-of-language-model-finetuning/