Expert Rating 3.0

SelcukTopal

Project Owner

A Logic-based Natural Language Interface (LNLI)

Expert Rating

3.0

Type SingularityNET RFP
Funding Request $60,000 USD
RFP Guidelines Utilize LLMs for modeling within MOSES

Overview

A Logic-based Natural Language Interface (LNLI) can proficiently leverage Large Language Models (LLMs) for both modeling and generation in the context of MOSES (Meta-Optimizing Semantic Evolutionary Search). By facilitating seamless interaction between users and the system, LNLI can interpret user inquiries, convert them into logic-based formats, and direct the evolutionary search mechanism. This methodology enables MOSES to navigate extensive solution landscapes effectively, producing optimized semantic models that align with user requirements, while maintaining scalability and adaptability through natural language inputs.

RFP Guidelines

Utilize LLMs for modeling within MOSES

Complete & Awarded

Type SingularityNET RFP
Total RFP Funding $150,000 USD
Proposals 10
Awarded Projects 1

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)

Web3 Decision

Project details

A Logic-based Natural Language Interface (LNLI) can provide solutions to utilizing Large Language Models (LLMs) within MOSES (Meta-Optimizing Semantic Evolutionary Search) by:

Facilitating Human-Machine Interaction: LNLI allows users to input complex logic-based queries or instructions in natural language, which the LLM can then translate into executable search and optimization tasks within MOSES. This interaction simplifies the model’s input process, enabling users to provide high-level semantic queries without understanding the underlying formal logic or optimization algorithms.
Semantic Interpretation: LNLI can be used to map natural language inputs into formal semantic representations that MOSES can use to guide evolutionary search. By understanding the meaning behind the user's natural language query, the LNLI allows the LLM to assist in guiding MOSES toward more meaningful optimization outcomes.
Dynamic Model Adaptation: LLMs can continuously evolve based on feedback from MOSES, adapting their internal models of natural language and logic. The LNLI can facilitate this by processing user feedback in a way that allows the LLM to adjust its models for more effective search and optimization.
Query Generation: The LLM can automatically generate queries for MOSES based on user needs, helping to evolve the search space or refine objectives without explicit manual specification. This reduces the burden on users and speeds up the optimization process.
Optimized Interpretation of Results: LNLI can also be used to help interpret the results of MOSES' semantic evolutionary search in a user-friendly way. The LLM can convert raw optimization data into clear natural language summaries or recommendations, enabling users to better understand the results and how they relate to their goals.

In short, LNLI enables a seamless and intuitive interaction with MOSES, making complex optimization tasks more accessible and efficient by leveraging LLMs for logic interpretation, generation, and semantic evolution.

Open Source Licensing

GNU GPL - GNU General Public License

Links and references

https://arxiv.org/abs/2308.08102

https://dl.acm.org/doi/abs/10.1145/1159842.1159858

https://www.cambridge.org/core/journals/review-of-symbolic-logic/article/abs/syllogistic-logic-with-cardinality-comparisons-on-infinite-sets/2E928E8C60185EC1BC8937AD943E899A

Activity Summary

Milestones

2

Total

Discussion

0

Total Comments

Reviews

3

Total Posted

Project Team

2

Total People

Total Milestones
2
Total Budget
$60,000_USD
Last Updated
25 Nov 2024

Milestone 1 - Project Initialization and Requirement Analysis

Description

Conduct a detailed requirement analysis for integrating LNLI with MOSES. Define key use cases, input-output specifications, and LLM interaction protocols. Identify datasets and ontologies needed for semantic interpretation. Duration: 1 month Budget: $7,000 Personnel (analysts, researchers): $4,500 Tools and software licenses (NLP tools, LLM APIs): $2,500

Deliverables

Requirement Analysis Report: A comprehensive document outlining the use cases, functional requirements, and technical specifications for the LNLI-MOSES integration. Project Plan: Detailed project timeline, milestones, resource allocation, and risk management strategy. Dataset and Ontology Identification: List of datasets and ontologies to be used for semantic interpretation and logical reasoning.

Budget

$7,000 USD

Milestone 2 - Total Budget: $53,000 USD

Description

Total Budget: $60,000 USD Project Initialization and Requirement Analysis: $7,000 LNLI Design and Architecture: $10,000 Development of NLU and Logic Translator Modules: $15,000 Integration with MOSES: $8,000 Testing and Optimization: $10,000 Documentation and Deployment: $5,000 Post-Deployment Support and Iteration: $5,000

Deliverables

Milestone 2: LNLI Design and Architecture System Architecture Diagram: High-level and detailed interactions between NLU, Logic Translator, NLG, and MOSES. APIs Design Document: Specifications for communication between LNLI modules and MOSES. Ontology Framework: Defined domain entities, relationships, and logic rules. Milestone 3: Development of NLU and Logic Translator Modules NLU Module: Parses inputs and identifies semantic elements. Logic Translator Module: Converts natural language to formal logic. Integrated LLM Functionality: Enables semantic parsing and logic mapping. Milestone 4: Integration with MOSES API Implementation: Operational APIs for LNLI-MOSES communication. Integration Framework: Interface linking LNLI logic to MOSES optimization processes. Integration Report: Challenges and solutions in integration. Milestone 5: Testing and Optimization Test Cases & Results: Documented test suite with outcomes. Optimization Report: LLM performance analysis with improvements. Usability Feedback: User recommendations for interface refinement. Milestone 6: Documentation and Deployment Technical Documentation: System, API references, and guides. User Manual: Simplified guide for end-users. Deployed System: LNLI-MOSES in production with monitoring. Milestone 7: Post-Deployment Support Bug Log: Resolutions of post-deployment issues. Feedback Report: User insights and improvement suggestions. Enhanced System: Updated LNLI-MOSES with refinements.

Budget

$53,000 USD

Join the Discussion (0)

Expert Ratings

Reviews & Ratings

Group Expert Rating (Final)

Overall

3.0

Compliance with RFP requirements 3.3
Solution details and team expertise 3.3
Value for money 3.3

Expert Review 1
Overall

2.0
- Compliance with RFP requirements 2.0
- Solution details and team expertise 4.0
- Value for money 0.0
The proposed use of LLMs makes sense but doesnt address the real challenges MOSES faces

This proposal suggests to use semantic parsing to create an NL interface to MOSES. This is possible to do with current LLMs and makes some sense for some applications, but it's very easy to do and doesn't really address the hard problems of MOSES which have to do with scalability of learning. The interesting ways to use LLMs to help MOSES would be to use the knowledge inherent in LLMs to help more effectively do semantic modeling of MOSEs populations to help guide search more effectively. Now I happen to know the proposer could do this more useful/difficult stuff also, but unfortunately it is not what has been proposed here...

Expert Review 2
Overall

4.0
- Compliance with RFP requirements 5.0
- Solution details and team expertise 3.0
- Value for money 0.0
Short but decent and on topic proposal.

That proposal is mainly focus on interfacing natural language with MOSES, i.e. describing a problem in NL that MOSES would understand, as well as rendering its results back in NL. So it is some kind of Semantic Parsing project, but geared toward interacting with MOSES. I suppose one would be able to use the same methodology to interact with deeper functions of MOSES, and then maybe even replace the human intereaction by an LLM (fulling the whole vision of the RFP), but that is not clearly mentioned in the proposal. So the proposal is not as ambitious as it could be, but that could be a plus, sometimes less is more.

Expert Review 3
Overall

3.0
- Compliance with RFP requirements 3.0
- Solution details and team expertise 3.0
- Value for money 0.0
The proposal’s goal makes sense as a tool for translating comple logic-based queries or instructions in natural language and turning them into executable search and optimization tasks. But there is little description on this will be done.

The weighted average of the 4 perspectives Overall

0.0
Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.Value for money

0.0
This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

0.0
RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.Solution details and team expertise

0.0

Review Headline

0 /50 chars

Review Summary

0 /5000 chars

The weighted average of the 4 perspectives Overall

3.0
Each RFP defines a maximum allowed budget, but teams can differentiate their proposal by offering a solution with a lower budget or a wider scope.Value for money

3.3
This rating indicates compliance to 'Must haves' but also adaptation of 'Nice to haves' and Non-functional requirements defined in the RFP.Compliance with RFP requirements

3.3
RFPs will offer varying degrees of freedom. This rating indicates the quality of the team's specific solution ideas, the provided details, and the reviewer's confidence in the team's ability to execute.Solution details and team expertise

3.3

Review Headline

0 /50 chars

0 /5000 chars

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Reviews and Ratings in Deep Funding are structured in 4 categories. This will ensure that the reviewer takes all these perspectives into account in their assessment and it will make it easier to compare different projects on their strengths and weaknesses. Overall (Primary) This is an average of the 4 perspectives. At the start of this new process, we are assigning an equal weight to all categories, but over time we might change this and make some categories more important than others in the overall score. (This may even be done retroactively). Feasibility (secondary) This represents the user\'s assessment of whether the proposed project is theoretically possible and if it is deemed feasible. E.g. A proposal for nuclear fission might be theoretically possible, but it doesn’t look very feasible in the context of Deep Funding. Viability (secondary) This category is somewhat similar to Feasibility, but it interprets the feasibility against factors such as the size and experience of the team, the budget requested, and the estimated timelines. We could frame this as: “What is your level of confidence that this team will be able to complete this project and its milestones in a reasonable time, and successfully deploy it?” Examples:

A proposal that promises the development of a personal assistant that outperforms existing solutions might be feasible, but if there is no AI expertise in the team the viability rating might be low.
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A metaverse project that spends 95% of its budget on the development of the game and only 5 % on the development of an AI service for the platform might expect a low ‘usefulness’ rating here.

A marketing proposal that creates t-shirts for a local high school, would get a lower ‘usefulness’ rating than a marketing proposal that has a viable plan for targeting highly esteemed universities in a scaleable way.
An AI service that is fully dedicated to a single product, does not take advantage of the purpose of the platform. When the same service would be offered and useful for other parties, this should increase the ‘usefulness’ rating.

SelcukTopal

Project Owner

logician, AI expert professional plays a critical role in this project, leveraging expertise in formal logic, AI modeling, and software development to ensure the successful implementation of LNLI.

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