Unlocking Real World Value: The Future of Open-Source Language Models


Hatched by Glasp

Sep 21, 2023

4 min read


Unlocking Real World Value: The Future of Open-Source Language Models

In the ever-evolving landscape of artificial intelligence, language models have become a powerful tool for various applications. However, there are certain challenges associated with these models, particularly in their ability to generate accurate and appropriate output. This has led to the development of Reinforcement Learning from Human Feedback (RLHF) techniques, which have proven to significantly improve the alignment and usability of language models.

One notable collaboration in this space is between Humanloop and Stability AI, who have joined forces to build the first open-source InstructGPT. This partnership aims to address the limitations of next word prediction models, which often produce factually inaccurate or offensive output. By incorporating RLHF techniques, Humanloop and Stability AI aim to create a language model that follows instructions and acts as a helpful assistant, unlocking its potential for real-world applications.

The concept of gatekept models has long limited the accessibility and applicability of language models. These models were primarily available to academics, hobbyists, and industry insiders, making it difficult for the general public to leverage their capabilities. However, with the advancement of RLHF techniques, we envision a future where these tuned models can be applied and adapted to every domain and task, unlocking tremendous real-world value.

To achieve this vision, partnerships are crucial. Carper AI has teamed up with Humanloop and Scale to collect and apply human feedback data that will be used to improve the underlying language model. Humanloop's expertise in adapting language models from human feedback, combined with Scale's leadership in data annotation, creates a robust foundation for enhancing the model's performance. Furthermore, Hugging Face's role in hosting the final trained model and making it generally accessible ensures that the benefits of these advancements reach a wider audience.

While the technical advancements in language models are essential, it is equally important to consider how users engage with content on the web. A study titled "How Users Read on the Web" highlights the significance of credibility in online information. Due to the anonymous nature of the web, users often struggle to determine the trustworthiness of a webpage. Factors such as high-quality graphics, good writing, and outbound hypertext links can significantly enhance credibility. Outbound links indicate that the authors have done thorough research and are willing to provide additional resources to readers.

Interestingly, the study reveals that users rarely read web pages word by word. Instead, they tend to scan the page, picking out individual words and sentences. In fact, 79 percent of the participants in the study admitted to scanning new pages they came across, while only 16 percent read word-by-word. This behavior can be attributed to the fast-paced nature of the internet, with users seeking to gather information quickly and efficiently.

To maintain credibility, it is crucial to avoid exaggerations and promotional language. Users are more likely to trust a website that presents information in a concise and factual manner. Hyperbolic language imposes a cognitive burden on users, forcing them to filter out the exaggerated claims to extract the relevant facts. By prioritizing clarity and accuracy, content creators can establish credibility and ensure that users have a positive experience on their websites.

In conclusion, the collaboration between Humanloop and Stability AI to build an open-source InstructGPT is a significant step towards unlocking the full potential of language models. By leveraging RLHF techniques and incorporating human feedback, these models can be fine-tuned to align with instructions and act as helpful assistants in various domains. Furthermore, the accessibility of these models through partnerships with Carper AI, Scale, and Hugging Face ensures that the benefits can be realized by a broader audience.

To make the most of these advancements, here are three actionable pieces of advice:

  • 1. Emphasize credibility: Invest in high-quality graphics, well-written content, and outbound hypertext links to establish trustworthiness and enhance the credibility of your webpages.
  • 2. Prioritize clarity: Users scan webpages, so present information in a concise and factual manner. Avoid promotional language and exaggerated claims, as they can undermine credibility and impose a cognitive burden on users.
  • 3. Leverage open-source language models: Explore the possibilities of RLHF-tuned models in your domain or task. Collaborate with organizations like Humanloop, Scale, and Hugging Face to collect and apply human feedback, unlocking the real-world value of these models.

With these strategies in place, we can navigate the evolving landscape of language models and harness their potential to revolutionize various industries and domains, ultimately benefiting both users and content creators alike.

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