Stanford Alpaca: A Small Yet Affordable Language Model for Instruction-Following Tasks
Over the past few months, instruction-following models like Chat GPT, Claude, and Bing Chat have taken the world by storm, but they are closed-source, and require massive amounts of computational resources to experiment with. These models are capable of generating text in response to prompts and are often used for tasks like writing assistance, chatbots, and content generation. However, as these models become more widespread, concerns have been raised about their limitations and risks.
These models can induce false information, propagate social stereotypes, and produce toxic language, among other issues. To address these concerns, it is important to engage with these models and explore ways to improve them. Unfortunately, this has been difficult in the past, as closed-source models like Open AI’s text-davinci-003 are not easily accessible for research purposes.
Meta AI’s Large Language Model (LLM) that prioritizes depth over parameter count, and its subsequent leak, allowed hackers to run LLMs on less powerful devices. This trend continues with Stanford University’s Centre for Research on Foundation Models developing Alpaca, an instruction-following LLM that can be retrained for new use cases at a modest cost.
Say hello to Alpaca
Alpaca is a small but capable 7B language model developed by researchers at Stanford University’s Centre for Research on Foundation Models. It was fine-tuned from Meta AI’s LLaMA 7B model and trained on 52K instruction-following demonstrations generated in the style of self-instruct using Open AI’s text-davinci-003. On the self-instruct evaluation set, Alpaca shows numerous behaviours analogous to Open AI’s text-davinci-003 but is also surprisingly small and easy/cheap to reproduce.
The creators of Alpaca, tried to take a unique and collaborative approach to ensure the model’s safety and ethical use. As part of this approach, they released the model’s training recipe and data, and plan to release the model weights in the future. But that’s not all — they launched a public demo of Alpaca for anyone to try.
But they took down their chatbot Alpaca AI due to concerns about rising costs, safety, and “hallucinations,” which occur when the chatbot confidently provides false information. Although the working version of Alpaca is no longer available, the code and data used to develop the model are still accessible on GitHub.
Along with the demo, the following were also published :
- Alpaca’s training data
- the code for its data generation process
- the training code used to fine-tune the model
By making these resources available, they are encouraging researchers to study the model’s behaviour and report any concerning behaviours that they observe, thereby demonstrating a commitment to transparency, safety, and ethical use of AI technology. This will allow us to mitigate any potential issues and improve the model’s performance. It also highlights the importance of collaboration and engagement within the academic community when it comes to developing safe and ethical AI models.
It is important to note that Alpaca is intended only for academic research, and any commercial use is prohibited. This restriction is due to Alpaca’s reliance on LLaMA which has a non-commercial license, the terms of use of OpenAI’s text-davinci-003 which prohibits the development of rival models, and the absence of adequate safety measures for deployment.
Why should you care about Alpaca?
Alpaca has the advantage of affordability, as it was developed for less than $600, unlike some of the larger models, which can cost tens of thousands of dollars to reproduce. Thus making it accessible to researchers and developers with limited budgets.
Also despite its small size and low cost, Alpaca has demonstrated impressive capabilities. Alpaca was used to generate text in response to user prompts, and it was found to be highly effective in generating coherent and relevant responses. Additionally, Alpaca was found to produce text that was more diverse and less repetitive than some of the larger models.
However, false information remains a significant issue with Alpaca, and there is still much work to be done in developing more responsible and effective instruction-following models that avoid generating false information, social stereotypes, and toxic language.
Instruction-following models have become ubiquitous tools in various industries, but they still have many deficiencies that need to be addressed. The failure of the Stanford Alpaca model, specifically designed to avoid generating false information) highlights the potential for further improvement, cost reduction and development of better instruction-following models. The issues of false information, social stereotypes, and toxic language are complex, and addressing them will require ongoing research and development.
Nevertheless, Alpaca with its small size and affordability represents a promising step towards access to powerful language models that can transform various industries. As hardware becomes smaller and retraining costs become cheaper, it is likely that more individuals and organizations will be able to leverage these models for their specific needs.
This democratization of access to powerful language models is a trend that is likely to continue in the future. As technology becomes more accessible, we can expect to see an increase in innovative applications and advancements in natural language processing.
By drawing inspiration from Alpaca’s achievements and challenges, researchers and developers can push the boundaries of what’s possible and create more responsible and effective language models for the benefit of society.
About the Author:
Geethu Suresh is a Microsoft .NET Consultant here at Version 1.
