Proposal to utilise LLMs for Modeling in MOSES

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aasavravi1234
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Proposal to utilise LLMs for Modeling in MOSES

Expert Rating

3.3

Overview

Our proposal aims to integrate Large Language Models (LLMs) into the MOSES framework to enhance its evolutionary algorithm capabilities. By leveraging LLMs, we will improve program generation modeling, cross-domain learning of fitness functions, and efficient fitness estimation. Our approach includes fine-tuning LLMs on MOSES program populations, abstracting patterns across diverse domains, and developing neural networks for fitness evaluation. The integration will reduce computational overhead, enhance program evolution, and provide a robust foundation for AGI development within the SingularityNET ecosystem.

RFP Guidelines

Utilize LLMs for modeling within MOSES

Complete & Awarded
  • Type SingularityNET RFP
  • Total RFP Funding $150,000 USD
  • Proposals 10
  • Awarded Projects 1
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SingularityNET
Oct. 9, 2024

This RFP invites proposals to explore the integration of LLMs into the MOSES evolutionary algorithm. Researchers can pursue one of several approaches, including generation modeling, fitness function learning, fitness estimation, investigation into domain-independent “cognitively motivated” fitness functions, or propose new innovative ways to leverage LLMs to enhance MOSES's capabilities within the OpenCog Hyperon framework.

Proposal Description

Company Name (if applicable)

Lasavo Labs

Project details

Proposal: Enhancing MOSES with Large Language Model Integration

 

Introduction

 

This proposal seeks to integrate Large Language Models (LLMs) into the MOSES (Meta-Optimizing Semantic Evolutionary Search) framework to enhance its program generation and fitness evaluation capabilities. MOSES, a key component of the OpenCog Hyperon ecosystem, is designed to evolve programs using probabilistic modeling techniques to optimize fitness functions. The integration of LLMs offers an unprecedented opportunity to improve MOSES's efficiency, scalability, and effectiveness by leveraging advanced natural language processing and deep learning methods.

 

Objectives

 

The primary goals of this proposal are:

 

1. Improve Program Generation: Use LLMs to replace or augment MOSES's Estimation of Distribution Algorithms (EDA) for program generation.

 

 

2. Enhance Fitness Function Design: Develop LLM-based tools for cross-domain fitness function learning and abstraction.

 

 

3. Optimize Fitness Estimation: Build LLM-powered neural networks to estimate program fitness more efficiently.

 

 

4. Foster Generalization: Enable cross-domain learning and adaptability within MOSES for diverse applications like genomics and financial prediction.

 

 

5. Advance AGI Development: Contribute to SingularityNET's broader AGI goals by enhancing MOSES's capabilities within the Hyperon framework.

 

 

 

 

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Technical Approach

 

1. LLM-Enhanced Program Generation

 

Problem: Traditional MOSES relies on EDAs to guide program evolution, which may not fully leverage the semantic insights of the evolved programs.

 

Solution: Integrate fine-tuned LLMs trained on the MOSES program population to predict promising program extensions.

 

LLMs will replace the EDA component, offering richer semantic modeling and program generation capabilities.

 

Example Use Case: Fine-tune an LLM on a population of programs evolved for a specific fitness function, then query it for potential program extensions.

 

 

 

2. Cross-Domain Fitness Function Learning

 

Problem: Manually designed fitness functions are domain-specific and computationally expensive.

 

Solution: Use LLMs to abstract patterns from multiple fitness functions, enabling cross-domain learning.

 

LLMs will generalize fitness functions across domains, identifying shared features and improving efficiency.

 

Example Use Case: Train LLMs on datasets from genomics and financial prediction problems to enable cross-domain transfer of fitness optimization strategies.

 

 

 

3. Neural Fitness Estimation

 

Problem: Fitness evaluation is computationally intensive and limits scalability.

 

Solution: Build LLM-based neural networks for rapid and accurate fitness estimation.

 

The neural networks will predict fitness values for programs, reducing the need for exhaustive evaluation.

 

Example Use Case: Develop a transformer-based model to estimate program fitness based on program structure and problem context.

 

 

 

4. Hybrid Evaluation System

 

Dynamic Evaluation: Combine LLM-based estimators with traditional methods for flexible and efficient fitness evaluation.

 

Implement a hybrid system that dynamically switches between LLM-based approximations and exact fitness evaluations based on uncertainty levels.

 

Example Use Case: Use LLMs for high-confidence predictions while reserving exact methods for ambiguous cases.

 

 

 

5. Integration with OpenCog Hyperon

 

Compatibility: Ensure seamless integration of LLMs into the MOSES framework and broader Hyperon ecosystem.

 

Use modular design principles and Atomese representations to bridge LLM-generated components with symbolic reasoning tools in Hyperon.

 

 

 

 

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Expected Outcomes

 

1. Technical Deliverables

 

LLM-enhanced MOSES implementation with modular architecture.

 

Neural fitness estimation library and plugins for Hyperon integration.

 

Comprehensive documentation, tutorials, and reproducible codebase.

 

 

 

2. Performance Improvements

 

Faster program evolution with improved diversity and quality.

 

Reduced computational costs for fitness evaluation.

 

Cross-domain learning capabilities enabling efficient generalization.

 

 

 

3. Broader Impact

 

Significant progress in AGI development by integrating neural and symbolic methods.

 

Contribution to SingularityNET’s ecosystem through enhanced AI tools.

 

 

 

 

 

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Research Plan and Timeline

 

Phase 1: Foundation (Months 1-6)

 

Fine-tune LLMs for program generation and fitness function learning.

 

Develop baseline neural fitness estimators.

 

Integrate initial components with the Hyperon framework.

 

 

Phase 2: Enhancement (Months 7-12)

 

Implement hybrid evaluation systems combining neural and traditional methods.

 

Optimize switching mechanisms for dynamic evaluation.

 

Conduct performance benchmarking in selected problem domains.

 

 

Phase 3: Validation (Months 13-18)

 

Validate LLM-integrated MOSES through large-scale empirical evaluation.

 

Compare results against traditional MOSES and neural program synthesis methods.

 

Document findings and publish results in peer-reviewed venues.

 

 

 

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Evaluation Criteria

 

1. Effectiveness

 

Improvement in program evolution efficiency and quality.

 

Accuracy and robustness of fitness estimators.

 

 

 

2. Innovation

 

Novelty of approaches to LLM integration and fitness function design.

 

Impact on AGI development within the Hyperon framework.

 

 

 

3. Feasibility

 

Alignment of proposed methods with MOSES and OpenCog Hyperon architectures.

 

Demonstrated ability to meet project milestones.

 

 

 

4. Cost-Effectiveness

 

Achieving significant advancements within the proposed budget and timeline.

 

 

 

5. Reproducibility

 

Comprehensive documentation and code availability for community validation.

 

 

 

 

 

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Risks and Mitigation Strategies

 

1. Technical Risks

 

Risk: LLMs may produce inaccurate or semantically incorrect program extensions.

 

Mitigation: Implement robust validation mechanisms and fallback methods to traditional MOSES components.

 

 

 

2. Integration Challenges

 

Risk: Difficulty in seamlessly integrating LLM components with the MOSES framework.

 

Mitigation: Use modular architectures and ensure compatibility with Atomese and Hyperon protocols.

 

 

 

3. Computational Costs

 

Risk: High resource requirements for LLM training and inference.

 

Mitigation: Leverage efficient model architectures, caching strategies, and selective evaluation.

 

 

 

 

 

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Resource Requirements

 

Personnel

 

2 Senior ML Researchers

 

2 OpenCog Developers

 

1 Project Manager

 

2 Research Assistants

 

 

Infrastructure

 

High-performance GPU cluster for LLM training.

 

Development workstations and storage systems.

 

 

 

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Budget Estimate

 

Personnel: $100,000

 

Infrastructure: $30,000

 

Miscellaneous: $20,000

 

Total: $150,000

 

 

 

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Conclusion

 

Integrating LLMs into MOSES offers a groundbreaking opportunity to advance program evolution and fitness evaluation, contributing to AGI development within the SingularityNET ecosystem. By leveraging state-of-the-art ne

ural and symbolic methods, this project will enhance MOSES’s capabilities, improve efficiency, and foster generalization across diverse problem domains. This proposal provides a detailed roadmap to achieve these goals, ensuring impactful and reproducible outcomes.

 

Links and references

 

1. MOSES Framework: MOSES Documentation

 

 

2. Hyperon Integration: Hyperon Framework

 

 

3. LLM Capabilities: OpenAI GPT-4, Hugging Face

 

 

4. Evolutionary AI: Survey on Evolutionary AI

 

 

5. Cross-Domain Learning: Transfer Learning Research

 

 

6. Community Resources: OpenCog Forum, SingularityNET Dev Portal

 

 

7. Benchmarks

: PSB Datasets.

 

 

 

Proposal Video

Not Avaliable Yet

Check back later during the Feedback & Selection period for the RFP that is proposal is applied to.

  • Total Milestones

    3

  • Total Budget

    $80,000 USD

  • Last Updated

    7 Dec 2024

Milestone 1 - LLM Integration Foundation

Description

Develop the foundational components for integrating LLMs into MOSES. This includes fine-tuning LLMs for program generation, designing initial neural fitness estimation models, and setting up compatibility with the MOSES framework.

Deliverables

Fine-tuned LLM model for program generation. Prototype of the neural fitness estimation component. Documentation of initial setup and integration process.

Budget

$20,000 USD

Success Criterion

Successful integration of a fine-tuned LLM with MOSES, with basic program generation demonstrated. Neural fitness estimation prototype achieves at least 70% accuracy in fitness predictions on test datasets.

Milestone 2 - Hybrid Evaluation System Development

Description

Develop the hybrid evaluation system combining LLM-based estimators with traditional MOSES evaluation. Optimize switching mechanisms for dynamic evaluation based on uncertainty thresholds.

Deliverables

Functional hybrid evaluation system integrated with MOSES. Switching mechanism implementation. Performance comparison reports between hybrid and baseline methods.

Budget

$30,000 USD

Success Criterion

Hybrid evaluation system demonstrates at least a 20% reduction in computational costs compared to traditional methods while maintaining comparable accuracy.

Milestone 3 - Validation and Benchmarking

Description

Conduct large-scale validation and benchmarking of the LLM-integrated MOSES framework across multiple domains (e.g., genomics, financial prediction). Publish findings and refine the system based on results.

Deliverables

Performance benchmarks and comparative reports. Reproducible codebase with comprehensive documentation. Peer-reviewed publication submission.

Budget

$30,000 USD

Success Criterion

Validation shows a minimum 30% improvement in program evolution efficiency and cross-domain adaptability. Codebase is publicly available and validated by at least two independent researchers.

Join the Discussion (0)

Expert Ratings

Reviews & Ratings

Group Expert Rating (Final)

Overall

3.3

  • Feasibility 4.3
  • Desirabilty 3.3
  • Usefulness 3.0
  • Expert Review 1

    Overall

    3.0

    • Compliance with RFP requirements 4.0
    • Solution details and team expertise 3.0
    • Value for money 3.0
    Basically makes sense but covers quite a lot of bases in a somewhat naive way

    This proposal offers to cover quite a lot of bases in a not so well specified way -- too many things to do viably given the time/money allotted I would say, it would be better to focus more. There are some strange arbitrary things here too, like positing a 70% accuracy on some fitness function (but which one?? is this good or bad?? depends on the problem...).... On the whole one gets the sense the proposer may be biting off more than they could chew, however they do seem to have the technical background to make an effort.

  • Expert Review 2

    Overall

    3.0

    • Compliance with RFP requirements 5.0
    • Solution details and team expertise 3.0
    • Value for money 3.0
    Good proposal, perhaps a bit under-specified and over-ambitious though.

    The proposal is good and on topic. It feels a bit generic to me but good nonetheless. I couldn't get information about the team. I don't understand the discrepancy between the funding request ($80K) and the budget section at the end ($150K).

  • Expert Review 3

    Overall

    4.0

    • Compliance with RFP requirements 4.0
    • Solution details and team expertise 4.0
    • Value for money 3.0

    Solid, comprehensive, and targeted proposal for using LLMs for modeling and generation within MOSES.

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