Researchers from the University of Washington and Allen Institute for AI propose a promising approach called Proxy-tuning, a decoding-time algorithm for fine-tuning large language models. It allows adjustments to model behavior without direct fine-tuning, addressing challenges in adapting proprietary models and enhancing model performance. The method offers more accessibility and efficiency, encouraging model-producing organizations to share output probabilities. [Word Count: 68]
“`html
The Power of Proxy-Tuning for Large Language Models
The capabilities of pretrained large language models are impressive, but achieving specific behaviors often requires additional adaptation. This becomes even more challenging when dealing with models whose weights are kept private, making tuning costly or impossible. Striking the right balance between customization and resource efficiency is crucial in optimizing the performance of these advanced language models.
The Solution: Proxy-Tuning
Researchers from the University of Washington and Allen Institute for AI present proxy-tuning, an algorithm designed to fine-tune large black-box language models without accessing their internal weights. This method leverages a smaller tuned LM and computes the difference between its predictions and the untuned version to adjust the original predictions of the larger base model, effectively achieving the benefits of direct tuning without altering the base model’s parameters.
Proxy-tuning significantly improves performance, reaching 88.0% on AlpacaFarm, 32.0% on GSM, and reducing toxicity to 0% on Toxigen. It also surpasses CHAT models in truthfulness. Across different scenarios, proxy-tuning closes 91.1% of the performance gap at the 13B scale and 88.1% at the 70B scale, demonstrating its effectiveness in enhancing model behavior without direct fine-tuning.
Practical Applications
Proxy-tuning emerges as a promising approach for fine-tuning large language models at decoding time by modifying output logits. It makes large language models more accessible, especially for those with limited resources, and addresses the challenge of adapting proprietary models to diverse use cases.
Evolve Your Company with AI
If you want to evolve your company with AI, stay competitive, and use AI to your advantage, consider the efficient alternative of proxy-tuning presented by the researchers from the University of Washington and Allen Institute for AI.
AI Solutions for Middle Managers
Discover how AI can redefine your way of work:
- Identify Automation Opportunities: Locate key customer interaction points that can benefit from AI.
- Define KPIs: Ensure your AI endeavors have measurable impacts on business outcomes.
- Select an AI Solution: Choose tools that align with your needs and provide customization.
- Implement Gradually: Start with a pilot, gather data, and expand AI usage judiciously.
Practical AI Solution: AI Sales Bot
Consider the AI Sales Bot from itinai.com/aisalesbot designed to automate customer engagement 24/7 and manage interactions across all customer journey stages.
For AI KPI management advice, connect with us at hello@itinai.com. For continuous insights into leveraging AI, stay tuned on our Telegram t.me/itinainews or Twitter @itinaicom.
“`
