LVec: Transforming LLMs into Strong Text Encoders

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Ahan M R
Project Owner

LVec: Transforming LLMs into Strong Text Encoders

Funding Requested

$5,000 USD

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Overview

LVec is proposed to change the way we interact with LLMs by vectorizing them and turning them into the word-level universal text encoders. This project will use the LVec method – a novel way to improve decoder-only LLMs for text embedding tasks. LVec will generate text encoders that are much more effective than the current approaches. This ideation phase will entail creation of the high-level and low-level architectural designs as well as the creation of an initial working prototype that will demonstrate potential of LVec.

Proposal Description

Our Team

Our team includes machine learning experts with extensive experience in training, fine-tuning, and deploying large language models (LLMs). They are proficient in the latest advancements in natural language processing (NLP) and deep learning techniques. We have a history of successfully completing complex AI projects, including Evaluation systems, predictive analytics, and real-time data processing applications.

View Team

Please explain how this future proposal will help our decentralized AI platform grow and how this ideation phase will contribute to that proposal.

By developing LVec, we will significantly contribute to the SNET ecosystem:

  1. Advanced Capabilities: The main product of LVec will be the modern text encoders that take into account the context and can be used for different services based on the AI systems on SNET. 
  2. Text-embedding models convert a piece of text, such as a search query, document, or piece of code, into a sequence of real-valued numbers. Given such embeddings, we can measure the similarity, or relatedness, of pieces of text. This facilitates various important applications, such as search, clustering, retrieval, and classification.
  3. LVec will a simple unsupervised approach that can transform any pretrained decoder-only LLM into a strong text encoder
  4. Foundation for Future Projects: This ideation stage will set the conditions and provide a framework for future growth, with more substantial investments and additional development rounds towards maturity.

Clarify what outcomes (if any) will stop you from submitting a complete proposal in the next round.

Insufficient Performance Improvements: If the prototype does not provide significant performance enhancements relative to current text encoders, then this would suggest that the LLM2Vec method might be less effective than expected.

The core problem we are aiming to solve

The core problem LVec aims to solve is the inefficiency and limitations of current text embedding models. Traditional methods struggle to provide rich, contextualized representations required for various natural language processing (NLP) tasks, such as semantic similarity, information retrieval, and clustering. There is a significant need for a more powerful, universal text encoding solution that can leverage the capabilities of large language models. Hence, we propose a way to use any decoder-based LLMs as Vector Embedding text models.

Our specific solution to this problem

LVec addresses this problem by transforming decoder-only LLMs into effective text encoders using the LLM to Vectorize approach. This method involves:

  • Enabling Bidirectional Attention: Modifying the attention mechanism to allow tokens to attend to all other tokens in the sequence.
  • Masked Next Token Prediction (MNTP): Training the model to predict masked tokens using both past and future contexts.
  • Unsupervised Contrastive Learning (SimCSE): Learning better sequence representations through contrastive learning techniques.

This approach ensures that LLMs can produce rich, contextualized embeddings suitable for various NLP tasks, outperforming traditional encoder-only models.

Project details

LVec will transform the landscape of text embedding by leveraging the inherent strengths of large language models. The project will unfold in several stages:

  • High-Level Architecture Design: Define the overall structure and components needed for LVec, including the interaction between LLMs, bidirectional attention mechanisms, MNTP, and SimCSE.
  • Low-Level Architecture Design: Detail the specific implementations, including data flow, model training processes, and integration points with existing systems.
  • Initial Prototype Development: Build a working prototype demonstrating the effectiveness of the LVec approach. This will involve selecting appropriate LLMs, implementing bidirectional attention, training with MNTP, and applying unsupervised contrastive learning.
  • Evaluation and Testing: Evaluate the prototype using standard benchmarks and real-world datasets to validate performance improvements and gather feedback for further refinements.

Existing resources

We will leverage several existing technologies and resources:

  • Pre-trained LLMs: Utilize pre-trained LLMs like GPT-4, LLaMA-2, and Mistral-7B to build upon their existing capabilities.
  • Data Repositories: Use publicly available datasets like Wikitext-103 for training and evaluation.
  • Development Tools: Employ development tools and frameworks such as Streamlit for UI, MongoDB for data storage, and Docker and Kubernetes for containerization and orchestration.

Proposal Video

Placeholder for Spotlight Day Pitch-presentations. Video's will be added by the DF team when available.

  • Total Milestones

    3

  • Total Budget

    $5,000 USD

  • Last Updated

    20 May 2024

Milestone 1 - High-Level Architecture Design and ideation

Description

Define the overall architecture and component interactions

Deliverables

In this phase the team will outline the major components of the LVec system focusing on how each part will interact. This includes defining the roles of LLMs bidirectional attention mechanisms masked next token prediction (MNTP) and unsupervised contrastive learning (SimCSE). The goal is to establish a clear cohesive vision for the system's architecture (ii)

Budget

$1,500 USD

Milestone 2 - Low-level Architecture and Development

Description

Detail the specific implementations and data flows

Deliverables

(i) Building on the high-level design the low-level architecture phase involves specifying the technical details of the implementation. This includes data flow diagrams model training processes integration points with existing systems and the specifics of how bidirectional attention and MNTP will be implemented. The low-level design ensures that every component is meticulously planned and ready for development.

Budget

$2,500 USD

Milestone 3 - Evaluate the approach using benchmarks & datasets

Description

Gather feedback and refine the prototype idea based on evaluation results

Deliverables

The final step in the ideation phase is preparing a comprehensive report and presentation of the findings. The report will outline the project's progress results from the prototype evaluation and proposed next steps for the complete proposal. All teams will contribute to this task and it is expected

Budget

$1,000 USD

Join the Discussion (1)

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1 Comment
  • 0
    commentator-avatar
    Gombilla
    Jun 5, 2024 | 4:23 PM

    Hi there. Great job ideating this. I would want to comment that your proposed method for transforming decoder-only LLMs into effective text encoders involves several intricate steps, including modifying attention mechanisms, masked token prediction, and contrastive learning techniques. Implementing these changes may require significant computational resources, expertise in deep learning, and thorough testing to ensure effectiveness and stability. Good luck ideating this !

Reviews & Rating

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3 ratings
  • 0
    user-icon
    CLEMENT
    Jun 5, 2024 | 4:27 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 5
    Potential for numerous practical applications

    Cheers mate !

    I see the outcome of this proposal to provide multiple practical applications. The development of more effective text encoders through LVec could have practical applications across industries, including healthcare, finance, and e-commerce. By providing better representations of textual data, LVec enables more accurate analysis, decision-making, and automation, driving value creation and efficiency improvements in real-world scenarios.

    Kudos to you and your team !

    Also, you are also welcomed to make comments on our team proposal as well

    https://deepfunding.ai/proposal/4757/  - AI4M (Enhancing Malaria Predictability using AI)

    https://deepfunding.ai/proposal/biotek-nexus-next-gen-biodiversity-conservation/  - BIOTEK NEXUS (Blockchain Biodiversity Conservation)

  • 0
    user-icon
    Tu Nguyen
    May 30, 2024 | 1:38 PM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 4
    Transforming LLMs Into Strong Text Encoders

    The core problem that this proposal aims to solve is the ineffectiveness and limitations of current text embedding models. This proposal has the idea to solve this problem by converting the decoder-only LLM into an efficient text encoder using the LLM to Vectorize approach. Information about milestones is quite detailed, but they should clearly identify the start and end times of the milestones.

  • 0
    user-icon
    Max1524
    Jun 10, 2024 | 2:28 AM

    Overall

    4

    • Feasibility 4
    • Viability 4
    • Desirabilty 4
    • Usefulness 5
    Should be a commitment to quality implementation

    The team has other proposals in this DFR4, will having more than 1 such proposal have any impact on the quality of proposal implementation? I assume all proposals are approved for funding. The team should reaffirm this with a firm commitment to quality performance.

Summary

Overall Community

4

from 3 reviews
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  • 4
    3
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  • 2
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  • 1
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Feasibility

4

from 3 reviews

Viability

4

from 3 reviews

Desirabilty

4

from 3 reviews

Usefulness

4.7

from 3 reviews

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