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.
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
LLM2LLM: Boosting LLMs with Novel Iterative Data Enhancement Significantly enhances the performance of LLMs in the low-data regime, outperforming various baselines (e.g. up to 24.2% improv. on GSM8K) https://t.co/GKPMTzze4M https://t.co/eD1fEvHEKd
Can large language models explore in-context? We investigate the extent to which contemporary Large Language Models (LLMs) can engage in exploration, a core capability in reinforcement learning and decision making. We focus on native performance of existing LLMs, without https://t.co/j43ESy9my1
[CL] LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models https://t.co/PJdTtFCTVW - The paper presents LLAMAFACTORY, a unified framework to integrate efficient fine-tuning methods for LLMs. It supports adapting 100+ LLMs on 50+ datasets. - LLAMAFACTORY… https://t.co/TuZyu2Kv6p
Any good programming model for LMs will make the choice to compile your code into things like automatic few-shot prompts or into finetuning with optimized synthetic data *completely* independent of your system design. I’ll know we succeeded when this becomes basic table stakes. https://t.co/90rkE2fYtO
Extremely hot LLM take: you will often get better results with few-shot prompting (with good examples) on a modern LLM than with a finetuned LLM. Finetuning was the best option for weaker LLMs with lower context windows: both problems have been solved nowadays.
Interesting takeaway from the LlamaFactory paper (https://t.co/LAXl17RFqb): Despite all the galore surrounding new training/finetuning methods, LoRA is still the go-to method for its performance in speed and model output quality. https://t.co/6TAxneoBxk
LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models We present LlamaFactory, a unified framework that integrates a suite of cutting-edge efficient training methods. It allows users to flexibly customize the fine-tuning of 100+ LLMs. https://t.co/3Y0Kx8NhK3