Recent advancements in AI research, particularly involving Large Language Models (LLMs), have demonstrated significant performance enhancements through various fine-tuning techniques. Google's latest research highlights the effectiveness of 'many-shot' in-context learning, which utilizes larger context windows to improve model performance without extensive human-generated data. Additionally, new methods like Parameter-Efficient Fine-Tuning (PEFT) and Proxy-Tuning allow for adapting pre-trained models to specific tasks by altering only a subset of parameters, thus conserving computational resources. Another notable development is the ability to self-extend LLM context windows without fine-tuning, further enhancing model adaptability and addressing out-of-distribution issues. These developments suggest a shift toward more efficient and capable AI systems.
[CL] Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data https://t.co/IAvOOWtLmw - This paper aims to understand the behaviors of various procedures for fine-tuning language models with preference data, including RL, maximum likelihood, and… https://t.co/hwenQj1sMm
[LG] Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey https://t.co/ntyEo4dj79 - Large models have achieved remarkable performance but require substantial computational resources for fine-tuning. Parameter Efficient Fine-Tuning (PEFT) provides a… https://t.co/N6J2xeG4gd
This AI Paper from Google DeepMind Introduces Enhanced Learning Capabilities with Many-Shot In-Context Learning Quick read: https://t.co/eRTTRHUdkz Researchers from Google Deepmind have introduced a shift toward many-shot ICL, leveraging larger context windows of models like…
📌 Pretty interesting proposal in this paper - Finetuning an LLM without actually training its own weights. 📌 "Tuning Language Models by Proxy" 🔥 Proxy-Tuning relies on a setup where you have a large LLM that you don't want to/can't fine-tune, and a pair of small LLMs that… https://t.co/qN8UKwYM0Z
"Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey" 📌 Parameter-Efficient Fine-Tuning (PEFT): The core concept revolves around adapting pre-trained large models to specific tasks by modifying only a small subset of parameters, leaving the majority of the… https://t.co/7MwXQUFsen
The self-extend paper is really becoming important - "LLM Maybe LongLM: Self-Extend LLM Context Window Without Tuning" 🔥 📌 Extend existing LLMs’ context window without any fine-tuning 📌 One feasible way to avoid the O.O.D. ( out-of-distribution) problems by caused unseen… https://t.co/XOvttXNEQN
ScholarAI In Action: Literature Review ✨ Feature deep dive 👇 #gpt4 #ai #literaturereview #phd #research https://t.co/DmUL4TrWUm
This paper from Google explores the potential of "many-shot" in-context learning with LLMs, exploring its effectiveness and limitations across various tasks, as well as ways to mitigate the need for extensive human-generated data. And finds significant performance boosts from… https://t.co/K7iZ8SR0mU
Integrating Large Language Models with Graph Machine Learning: A Comprehensive Review Quick read: https://t.co/qLWhUxk4lN Paper: https://t.co/ZVPEv401FW #ArtificialIntelligence #DataScience https://t.co/DyREhyCOP7
Very large context windows may extend the capabilities of LLMs because you can give them hundreds of examples on how to solve a problem (many shot learning). This paper from Google finds significant performance boosts from many shot, even when the AI generates its own examples. https://t.co/9EPQowdvrw