"Optimizing Language Models for Dialogue and the Scarcity of Legitimacy"

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Aug 27, 2023

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"Optimizing Language Models for Dialogue and the Scarcity of Legitimacy"

Introduction:

Language models have come a long way in their ability to engage in dialogue and provide informative responses. One such model is ChatGPT, which leverages the dialogue format to answer follow-up questions, challenge assumptions, and even admit mistakes. However, like any language model, ChatGPT has its limitations and challenges. On the other hand, the concept of legitimacy plays a significant role in various social contexts, including the blockchain space. In this article, we will explore the optimization of language models for dialogue and delve into the importance of legitimacy as a scarce resource.

Optimizing Language Models for Dialogue:

ChatGPT, similar to other language models, has been trained using Reinforcement Learning from Human Feedback (RLHF). This involves collecting comparison data to create a reward model for reinforcement learning. AI trainers engage in conversations with the chatbot, and alternative completions of model-written messages are ranked for quality. Proximal Policy Optimization is then employed to fine-tune the model based on these reward models. This iterative process allows for continuous improvement in the model's responses.

Challenges and Limitations:

While ChatGPT has shown promise in its dialogue capabilities, it still faces several challenges. One challenge lies in the absence of a source of truth during RL training. Additionally, training the model to be cautious can lead to it declining questions it could answer correctly. Supervised training can also mislead the model, as the ideal answer depends on its knowledge rather than that of the human demonstrator. Furthermore, ChatGPT's sensitivity to input phrasing or slight rephrases can result in disparate responses. Ideally, the model should ask clarifying questions when faced with ambiguous queries instead of making assumptions.

The Scarcity of Legitimacy:

Legitimacy plays a crucial role in various social contexts, including the blockchain space. It is a pattern of higher-order acceptance, where outcomes are considered legitimate if they are broadly accepted and enacted by individuals who expect others to do the same. Legitimacy arises naturally in coordination games and governs social status, intellectual discourse, property rights, political systems, and more. The concept of legitimacy becomes particularly relevant when discussing the allocation of capital within ecosystems like Bitcoin and Ethereum.

Addressing Funding Imbalances:

Despite the ability of blockchain ecosystems to summon significant capital, restrictions on its allocation exist. Public goods necessary for the survival of capital receive limited funding. While some limitations arise from concerns about political chaos and capture, there is a need to develop better social technologies to fund public goods on a larger scale. One-off acts of philanthropy are insufficient, and more systemic approaches are required.

Harnessing NFTs for Public Goods Funding:

Non-Fungible Tokens (NFTs) offer a promising avenue for addressing the chronic funding deficiencies of public goods, particularly in the creative realm. By establishing legitimacy and societal value for NFTs, artists, charities, and other beneficiaries can access a more reliable channel of funding. Two potential ideas for leveraging NFTs include "blessing" NFTs in exchange for contributions to charitable causes and working with social media platforms to increase visibility and showcase the values associated with NFT purchases.

Actionable Advice:

  • 1. Continuously refine language models like ChatGPT by incorporating user feedback and addressing challenges such as ambiguity in queries.
  • 2. Explore innovative funding mechanisms, such as leveraging NFTs, to support public goods and address chronic funding deficiencies in various domains.
  • 3. Foster dialogue and collaboration between blockchain communities and organizations to develop better social technologies for funding public goods.

Conclusion:

Optimizing language models for dialogue, exemplified by ChatGPT, opens up new possibilities for human-like interactions. However, challenges persist, such as the need for source of truth during training and addressing model sensitivity. In parallel, the scarcity of legitimacy plays a pivotal role in the allocation of resources within ecosystems like Bitcoin and Ethereum. By recognizing and addressing this scarcity, we can develop more robust funding mechanisms, leveraging concepts like NFTs to support public goods and create a more equitable economic ecology.

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