Conformal Prediction with Large Language Models for Multi-Choice Question Answering
Project Overview
The document explores the integration of generative AI, particularly large language models (LLMs), in the education sector, focusing on their application in multiple-choice question answering (MCQA) tasks. It emphasizes the critical role of uncertainty quantification in enhancing the reliability and trustworthiness of AI systems, particularly in high-stakes educational environments. By employing conformal prediction, the analysis demonstrates how this method can effectively filter out low-quality predictions, thereby improving the overall accuracy of LLMs in educational assessments. The findings indicate that implementing such techniques not only bolsters the performance of AI in educational contexts but also fosters greater confidence in AI-driven tools among educators and learners alike. Ultimately, the document underscores the potential of generative AI to transform educational practices through more reliable and effective assessment methods.
Key Applications
Conformal Prediction with Large Language Models for Multi-Choice Question Answering
Context: Educational assessment and testing across various subjects including high school and college-level courses.
Implementation: Using LLaMA-13B model and MMLU benchmark to generate and evaluate MCQA questions, with a focus on uncertainty quantification through conformal prediction.
Outcomes: Improved accuracy in MCQA tasks by filtering out low-quality predictions based on uncertainty measures. Demonstrated strong correlation between prediction uncertainty and accuracy.
Challenges: Issues with model calibration, including under-confidence and over-confidence in predictions. Reliance on accurate calibration datasets.
Implementation Barriers
Technical Barrier
Current LLMs sometimes produce hallucinated or factually incorrect outputs, leading to issues in high-stakes decision-making. Implementing uncertainty quantification techniques such as conformal prediction can provide reliability and trustworthiness in outputs.
Proposed Solutions: Implementing uncertainty quantification techniques such as conformal prediction to provide reliability and trustworthiness in outputs.
Data Barrier
The need for extensive and high-quality calibration datasets for effective implementation of conformal prediction and developing methods to utilize smaller datasets or leveraging transfer learning for calibration.
Proposed Solutions: Developing methods to utilize smaller datasets or leveraging transfer learning for calibration.
Project Team
Bhawesh Kumar
Researcher
Charlie Lu
Researcher
Gauri Gupta
Researcher
Anil Palepu
Researcher
David Bellamy
Researcher
Ramesh Raskar
Researcher
Andrew Beam
Researcher
Contact Information
For information about the paper, please contact the authors.
Authors: Bhawesh Kumar, Charlie Lu, Gauri Gupta, Anil Palepu, David Bellamy, Ramesh Raskar, Andrew Beam
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