Proof of Influence in Large Language Models : POILLMs

[jmikedupont2]

Proof of Influence in Large Language Models : POILLMs This is ai generated with bing based on ramblings from me.

Whitepaper: Proof of Influence in Large Language Models (LLMs) Abstract The concept of Proof of Influence (PoI) within the context of large language models (LLMs) aims to address the following question: How can we fairly compensate artists and creators whose work contributes to the development and fine-tuning of AI-generated content? This whitepaper outlines a novel approach to track and reward artistic influence within LLMs, leveraging blockchain technology, smart contracts, and decentralized networks.

1. Introduction Large language models, such as GPT-4, have revolutionized natural language processing and content generation. However, the creative input of artists and domain experts often goes unrecognized. PoI seeks to bridge this gap by providing a mechanism for artists to monetize their influence on LLMs.

2. The Proof of Influence Framework 2.1. Artist Registration Artists register their unique styles, techniques, or elements with the PoI network.

Registration involves fine-tuning an LLM with the artist's work, creating a personalized model.

The artist's model is stored on the blockchain, ensuring transparency and immutability.

2.2. Model Storage Network The PoI network consists of specialized smart contracts, each representing a block of the artist's model.

Miners execute these contracts, running inference tasks and publishing results on IPFS.

Encrypted results are accessible upon payment, allowing clients to verify the model's usage.

2.3. Compensation Mechanism When an AI-generated piece incorporates an artist's style, the relevant weights are traced.

A second-layer network introspects these weights, identifying the artist's influence.

Compensation is automatically calculated based on usage and distributed in cryptocurrency.

3. Implementation Details 3.1. Gas Fees and Fine-Tuning Artists receive compensation for fine-tuning their models using gas fees.

Miners prioritize executing artist-specific blocks, incentivizing model creation.

Open-source developers can also participate, contributing to the ecosystem.

3.2. Licensing and Exclusive Contracts Clients license mixed models, combining multiple artists' work.

Exclusive contracts ensure that certain styles remain proprietary.

Custom models become the new NFTs, representing artistic influence.

4. Value4Value Integration Real-time streaming sats (satoshis) facilitate payments to artists and developers.

Morpheus network handles inference tasks, with a tax for self-improvement.

Source code chunks run over Morpheus, enhancing the network's capabilities.

5. Conclusion Proof of Influence introduces a fair and transparent system for compensating artists and creators. By combining LLMs, blockchain, and decentralized networks, we can revolutionize how influence is recognized and rewarded in the AI era.

Disclaimer: This whitepaper outlines conceptual ideas and does not constitute legal or financial advice. Implementation details may vary based on technical feasibility and community consensus.

The journey to the concept of Proof of Influence likely began with a recognition of the immense potential that large language models (LLMs) hold in the realm of content creation. Here's a possible narrative of the thoughts leading up to your idea:

Recognition of Artists' Contributions: The initial thought might have been the realization that artists and creators contribute significantly to the datasets used to train LLMs. Their unique styles and creative outputs are integral to the diversity and richness of AI-generated content.

Need for Fair Compensation: As AI becomes more prevalent in generating content, the question arises: How can we ensure that artists are fairly compensated for their influence on these models?

Blockchain as a Solution: Blockchain technology offers a transparent and immutable ledger, ideal for tracking contributions and ensuring that compensation is fairly distributed.

Smart Contracts for Automation: The idea of using smart contracts could have emerged as a way to automate the process of identifying an artist's influence and executing payments.

Decentralized Model Storage: To prevent central control and promote openness, a decentralized network for storing and executing models, like IPFS, might have been considered.

Cryptocurrency for Payments: Cryptocurrency, with its global reach and ease of transfer, would be a natural choice for compensating artists across borders without the need for traditional banking systems.

Second-Layer Networks for Introspection: The complexity of tracing influence within an LLM's weights could lead to the idea of a second-layer network that could analyze and attribute influence more effectively.

Custom Models as NFTs: The unique nature of each artist's contribution could be likened to the uniqueness of NFTs, leading to the idea of custom models being the new form of digital art ownership.

Value4Value and Real-Time Payments: The desire for a more dynamic and immediate compensation model could have inspired the integration of the value4value system, allowing for real-time payments to creators.

Self-Improving Network via Tax: Finally, the concept of a self-improving network, where a portion of the network's resources is used to further enhance its capabilities, could have been the culminating thought, tying together the need for continuous development with the principle of giving back to the community.

These thoughts represent a blend of appreciation for artistic work, a desire for fairness, and an innovative use of technology to create a sustainable ecosystem for creators in the age of AI. 🌐✨

[jmikedupont2]

the tracing will be done by composing larger models from pools of artists, also users can choose which artist to use with what weight and the 2lm will use that as input

That's an interesting approach to address the tracing challenge in Proof of Influence (PoI) for large language models (LLMs). Here's a breakdown of the idea and some potential implications:

Composing Larger Models from Artist Pools:

• Pros:

  • This method could simplify influence tracing by attributing outputs to the larger artist pool model.
  • It allows for a more nuanced influence spectrum, incorporating contributions from various artists.
  • Users have more control over the desired artistic style in the generated content.

• Cons:

  • Attribution to individual artists might become less precise.
  • Determining fair compensation for each artist within the pool model could be complex.
  • Users might require guidance on selecting weights for different artists, potentially introducing a learning curve.

User-Controlled Weights and 2LM (Two-Level Model) Input:

• Pros:

  • Users have greater control over the creative direction of the AI-generated content.
  • This approach caters to users with specific stylistic preferences.
  • It aligns with the concept of PoI by allowing users to directly influence the model they use.

• Cons:

  • The complexity of the system might increase, potentially impacting efficiency.
  • Users with limited knowledge of artistic styles might struggle to choose weights effectively.
  • Ensuring a balance between user control and accurate attribution within the 2LM remains a challenge.

Overall:

This composable model with user-controlled weights adds an interesting layer to PoI. It offers more flexibility and user control, but also introduces new challenges around attribution and potentially, efficiency. Here are some additional thoughts:

• Transparency in Weighting: Providing users with clear information on how weights influence the generated content is crucial.
• Default Weighting Options: Pre-defined weighting options based on artistic styles could be helpful for novice users.
• AI-assisted Weighting: Machine learning techniques could be explored to recommend weights based on user preferences.

Further exploration is needed to determine the optimal balance between user control, accurate attribution, and computational efficiency within the PoI framework. It's definitely a step towards a more user-centric and customizable approach to AI-generated content.

[jmikedupont2]

so the merging algorithm of the models can then add meta data to reverse the merge and find out which artist influences which variable the most

You're right, the merging algorithm in the Proof of Influence (PoI) framework for LLMs can potentially be designed to incorporate metadata that aids in influence tracing. Here's how it might work:

Merging Algorithm with Influence Tracking Metadata

1. Artist Model Preprocessing:

   • Each artist registers their style with the PoI network.
   • An artist model is created by fine-tuning a base LLM with the artist's work.
   • During this fine-tuning process, the model's parameters (variables) are tagged with metadata. This metadata can include:
     • Artist ID: Uniquely identifies the artist associated with the parameter.
     • Contribution Type: Categorizes the artist's influence (e.g., vocabulary, syntax, stylistic elements).
   • The preprocessed artist model with embedded metadata is stored securely on the blockchain.

2. Merging with Weighted Contribution:

   • When composing a larger model from multiple artists, a merging algorithm is employed.
   • This algorithm considers weights assigned by the user, determining the relative influence of each artist.
   • During merging, the weights are applied not just to the final model output, but also to the internal parameter values.
   • This weighted merging process preserves the influence traceability within the combined model.

3. Reverse Merging and Influence Attribution:

   • To determine which artist influences a specific variable in the merged model, a reverse merging process can be employed.
   • This involves analyzing the metadata associated with the variable.
   • By examining the Artist ID and Contribution Type tags, the system can identify the artist(s) who most significantly influenced that particular variable.

Challenges and Considerations:

• Metadata Granularity: The level of granularity in the metadata can impact accuracy. Finer-grained tagging (e.g., word-level vs. sentence-level) provides more precise attribution but increases complexity.
• Computational Overhead: Embedding and processing metadata adds computational cost to the merging and reverse merging processes.
• Interpretability of Weights: Weighting schemes need to be carefully designed to ensure they accurately reflect artistic influence on the final output.

Potential Benefits:

• Accurate Attribution: This approach can provide a more accurate way to track the influence of individual artists on specific aspects of the generated content.
• Fairer Compensation: By pinpointing the contribution of each artist to the merged model, PoI can distribute rewards more fairly based on the level of influence.
• Transparency and Trust: Increased transparency in influence attribution can foster trust within the PoI ecosystem.

Conclusion:

Incorporating metadata into the merging algorithm is a promising direction for PoI. It can address the challenge of attributing influence in composable models while ensuring fairer compensation for artists. However, careful consideration needs to be given to the trade-offs between metadata granularity, computational efficiency, and interpretability of weights.

[jmikedupont2]

In the proposed Proof of Influence (PoI) system for large language models (LLMs), mixed models (models combining influences from multiple artists) wouldn't directly purchase inference from individual artists or "artist miners." Here's a breakdown of how it might work:

1. Artist Model & Decentralized Storage:

• Artists register their unique styles with the PoI network.
• Each artist creates a personalized model by fine-tuning a base LLM with their work.
• These artist models are stored securely on a decentralized network like IPFS (InterPlanetary File System).

2. Artist Influence & Smart Contracts:

• The artist models don't contain the raw weights (parameters) directly accessible to mixed models.
• Smart contracts manage access to the artist models and their influence on mixed models.

3. Inference with Encrypted Results:

• When a user wants to generate content using a mixed model, they specify the desired artistic styles and their relative weights.
• The system retrieves the relevant artist models from the decentralized network.
• Inference happens within the network, not on the user's device. Encrypted results are returned, ensuring the raw weights remain hidden.

4. Compensation & Attribution:

• The smart contracts track how the artist models contribute to the final output.
• Based on this tracked influence, artists are compensated with cryptocurrency.

Benefits:

• Artist Control: Artists retain control over their core contribution (the model).
• Security: Raw weights remain secure, preventing unauthorized copying of artistic style.
• Transparency: Smart contracts ensure clear attribution and fair compensation for artists.

What Mixed Models Purchase:

• Mixed models wouldn't purchase inference directly from artists, but rather:
  • Access to a License: They might purchase a license to use specific artist models within the mixed model for a certain period.
  • Inference Calls: They could pay for the number of inference calls they make using the mixed model (which leverages the influence of multiple artists).

Overall:

PoI aims to create a system where artists are compensated for their influence without compromising the security of their core contribution. Mixed models pay for the ability to leverage this influence, not the raw data itself.