LLM is All You Need : How Do LLMs Perform On Prediction and Classification Using Historical Data

Author(s)	Yuktesh Kashyap, Amrit Sinha
Country	India
Abstract	This study investigates the utility of large language models (LLMs) in performing traditional machine learning tasks such as prediction, and explores the potential of refinement architectures to enhance their effectiveness in these roles. Utilizing the Titanic survival dataset, we conducted a com- parative analysis using both conventional machine learning tools and LLM-based approaches. Our findings indicate that while LLMs differ fundamentally from traditional ML models in prediction tasks, there exist specific architectural modifications, termed Thought Refinement Architectures, which can significantly improve their performance. These results highlight the potential for inte- grating LLMs into traditional ML workflows, thereby expanding their applicability and enhancing predictive accuracy.
Keywords	Artificial Intelligence; Machine Learning; Large Language Models; Prompting Techniquw
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 6, Issue 3, May-June 2024
Published On	2024-06-27
Cite This	LLM is All You Need : How Do LLMs Perform On Prediction and Classification Using Historical Data - Yuktesh Kashyap, Amrit Sinha - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.23438
DOI	https://doi.org/10.36948/ijfmr.2024.v06i03.23438
Short DOI	https://doi.org/gt24x6

About IJFMR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us		+91-9898-5948-55	editor@ijfmr.com

International Journal For Multidisciplinary Research