Large language models (LLMs) are powerful tools for natural language processing (NLP) tasks. However, their performance often suffers in low-data scenarios due to limited training data. This project investigates the potential of integrating LLM2LLM, a novel iterative data augmentation technique, with LangTest to improve LLM fine-tuning in low-data regimes.
Objectives:
Understand the LLM2LLM approach and its effectiveness in boosting LLM performance with limited data.
Analyze LangTest's capabilities for LLM fine-tuning, data integration, and error analysis.
Develop and evaluate strategies for integrating LLM2LLM with LangTest for data augmentation.
Assess the impact of LLM2LLM-generated synthetic data on LLM performance within LangTest.
Methodology:
LLM2LLM Exploration:
Thoroughly study the research paper on LLM2LLM, focusing on:
The workflow (fine-tuning, error identification, synthetic data generation, integration).
Evaluation metrics used in the paper (accuracy improvements on specific datasets).
Potential limitations or considerations regarding synthetic data generation.
LangTest Analysis:
Investigate LangTest functionalities to identify areas for potential integration with LLM2LLM. Consider:
Can LangTest perform custom fine-tuning of LLMs on user-provided datasets?
Does LangTest offer functionalities to analyze errors made by an LLM during evaluation?
Can LangTest integrate synthetic data generated by an external source (teacher LLM)?
Integration Strategy Development:
Brainstorm potential strategies for integrating LLM2LLM with LangTest, such as:
Pre-processing with LLM2LLM: Utilize LLM2LLM to generate synthetic data before feeding it into LangTest for fine-tuning.
Error Analysis with LLM2LLM: Leverage LangTest for LLM evaluation and utilize LLM2LLM to analyze specific errors. Integrate the synthetic data generated from these errors into LangTest for further fine-tuning.
Comparison Framework: Develop a framework to assess LLM performance within LangTest with and without LLM2LLM data augmentation.
Feasibility Assessment and Experiment Design:
Evaluate the feasibility of each integration strategy based on LangTest's capabilities.
Design experiments to evaluate the chosen strategy, considering:
Defining evaluation metrics aligned with LangTest's functionalities (e.g., accuracy improvement on specific NLP tasks).
Conducting experiments comparing LLM performance with and without LLM2LLM data augmentation.
Documentation and Sharing:
Document the chosen integration strategy, experimental setup, and results.
Consider sharing your findings with the LangTest community or relevant NLP forums.
Expected Outcomes:
Gain a deeper understanding of LLM2LLM and its potential for low-data NLP.
Identify effective strategies for integrating LLM2LLM with LangTest for data augmentation.
Evaluate the impact of LLM2LLM on LLM performance within LangTest.
Contribute to the development of improved fine-tuning techniques for low-data NLP tasks.
Resources:
Research paper on LLM2LLM (if publicly available)
LangTest documentation and tutorials
Abstract:
Large language models (LLMs) are powerful tools for natural language processing (NLP) tasks. However, their performance often suffers in low-data scenarios due to limited training data. This project investigates the potential of integrating LLM2LLM, a novel iterative data augmentation technique, with LangTest to improve LLM fine-tuning in low-data regimes.
Objectives:
Methodology:
LLM2LLM Exploration: Thoroughly study the research paper on LLM2LLM, focusing on:
LangTest Analysis: Investigate LangTest functionalities to identify areas for potential integration with LLM2LLM. Consider:
Integration Strategy Development: Brainstorm potential strategies for integrating LLM2LLM with LangTest, such as:
Feasibility Assessment and Experiment Design:
Documentation and Sharing:
Expected Outcomes:
Resources:
Research paper on LLM2LLM (if publicly available) LangTest documentation and tutorials