Investigating the Efficacy of Large Language Models in Reflective
  Assessment Methods through Chain of Thoughts Prompting
Baphumelele Masikisiki Vukosi Marivate Yvette Hlope
Large Language Models, such as Generative Pre-trained Transformer 3 (aka.
GPT-3), have been developed to understand language through the analysis of
extensive text data, allowing them to identify patterns and connections between
words. While LLMs have demonstrated impressive performance across various
text-related tasks, they encounter challenges in tasks associated with
reasoning. To address this challenge, Chain of Thought(CoT) prompting method
has been proposed as a means to enhance LLMs' proficiency in complex reasoning
tasks like solving math word problems and answering questions based on logical
argumentative reasoning. The primary aim of this research is to assess how well
four language models can grade reflective essays of third-year medical
students. The assessment will specifically target the evaluation of critical
thinking skills using CoT prompting.
  The research will provide the following contributions; to introduce and
educate on the process of instructing models to evaluate reflective essays from
a dataset they have not been previously trained on; to illustrate the use of
CoT prompting as an instructional approach for training large models to carry
out particular tasks. Our results suggest that among all the models, Llama-7b
performs the least effectively, displaying the highest mean squared error.
Conversely, ChatGPT emerges as the superior model, boasting a higher Cohen
kappa score value of 0.53. Lastly, it's important to note that the selected
models do prioritise user privacy by allowing users to delete their own
conducted conversations.