Exploring LLM2LLM for Data Augmentation

[chakravarthik27]

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:

• 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