Can Active Label Correction Improve LLM-based Modular AI Systems?
Project Overview
The document explores the role of Active Label Correction (ALC) in enhancing the efficacy of modular AI systems that utilize large language models (LLMs) such as GPT-3.5 in educational contexts. It addresses the common challenges these systems face, including the prevalence of noisy annotations generated by LLMs and the high computational demands of training multiple fine-tuned models. ALC is introduced as a solution to improve data quality by incorporating human feedback to rectify misannotations, which in turn streamlines the training process for task-specific models. The findings demonstrate that ALC significantly reduces the volume of data essential for effective training while still achieving high performance in natural language processing (NLP) tasks. Overall, the document underscores the potential of generative AI, particularly through ALC, to optimize educational applications by enhancing model training efficiency and accuracy, thereby contributing to more effective learning experiences.
Key Applications
Active Label Correction (ALC) and ALC3
Context: Improving the performance of modular AI systems using LLMs in various NLP tasks.
Implementation: Collecting noisy data traces from AI systems and applying ALC to iteratively correct misannotations with human feedback.
Outcomes: Achieved oracle performance with 17-24% fewer labeled examples needed compared to the number of misannotated examples.
Challenges: Quality of LLM annotations can be low, and human feedback is required which can be costly.
Implementation Barriers
Technical Barrier
Noisy annotations from LLMs lead to inaccuracies in training data.
Proposed Solutions: Using Active Label Correction (ALC) to improve data quality by leveraging human feedback.
Resource Barrier
Computational costs associated with fine-tuning multiple models is high.
Proposed Solutions: Training smaller task-specific models that can replace LLM calls to reduce costs.
Project Team
Karan Taneja
Researcher
Ashok Goel
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Karan Taneja, Ashok Goel
Source Publication: View Original PaperLink opens in a new window
Project Contact: Dr. Jianhua Yang
LLM Model Version: gpt-4o-mini-2024-07-18
Analysis Provider: Openai