Sources
Loading...
Additional media
Loading...
DeepNewz, mobile.
People-sourced. AI-powered. Unbiased News.
The answer is a bit more complex and also connects to the recent skepticism about 7B models. Let me answer this question based on my experience: Question: How powerful is LLM fine-tuning? Answer: Unbelievably Insane. It will 100% surprise you when it works well. The catch… https://t.co/lRhTV4VtPV
[CL] LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement N Lee, T Wattanawong, S Kim, K Mangalam... [UC Berkeley] (2024) https://t.co/UT2MV0Aw0V - The paper proposes LLM2LLM, an iterative data augmentation technique that uses a teacher LLM to expand a small seed dataset… https://t.co/L3svjluzG2
LLM2LLM Boosting LLMs with Novel Iterative Data Enhancement Pretrained large language models (LLMs) are currently state-of-the-art for solving the vast majority of natural language processing tasks. While many real-world applications still require fine-tuning to reach https://t.co/vCKvsIwEXb
Researchers have introduced innovative methods for fine-tuning Large Language Models (LLMs) such as LlamaFactory and LLM2LLM. LlamaFactory offers customizable training for over 100 LLMs, while LLM2LLM enhances LLM performance with iterative data enhancement. Despite advancements, few-shot prompting can outperform finetuning on modern LLMs.