  The ease of using a Large Language Model (LLM) to answer a wide variety of
queries and their high availability has resulted in LLMs getting integrated
into various applications. LLM-based recommenders are now routinely used by
students as well as professional software programmers for code generation and
testing. Though LLM-based technology has proven useful, its unethical and
unattributed use by students and professionals is a growing cause of concern.
As such, there is a need for tools and technologies which may assist teachers
and other evaluators in identifying whether any portion of a source code is LLM
generated.
  In this paper, we propose a neural network-based tool that instructors can
use to determine the original effort (and LLM's contribution) put by students
in writing source codes. Our tool is motivated by minimum description length
measures like Kolmogorov complexity. Our initial experiments with moderate
sized (up to 500 lines of code) have shown promising results that we report in
this paper.
