Update README.md

README.md

@@ -17,20 +17,20 @@ base_model:
 
 ### Introduction
 
-According to the August 2025 jobs report, the US economy gained 22,000 jobs in August, which is significantly lower than projected by economists. 
-Overall unemployment has risen, with the unemployment rate for workers aged 16-24 rising to 10.5% (Bureau of Labor Statistics, 2025). The primary demographic 
+According to the August 2025 jobs report, overall unemployment has risen, with the unemployment rate for workers aged 16-24 rising to 10.5% (Bureau of Labor Statistics, 2025). The primary demographic 
 of this age range is recent college graduates, many of whom carry student loan debt and are unable to find stable, long-term employment. While this could be 
-attributed to any of the various economic challenges facing the US today, which ironically include AI development, there is speculation that it may 
-also be due to insufficient skills regarding job-hunting and interviews. There are many resources that seek to fill this gap, from job-posting sites 
-including LinkedIn (LinkedIn, 2025), to interview-prep LLMs such as InterviewsPilot (InterviewsPilot, 2025). However, there is not an LLM that 
+attributed to any of the various economic challenges facing the US today, there is speculation that it may 
+be due to insufficient skills regarding job-hunting and interviews. There are many resources that seek to fill this gap, including interview-prep LLMs such as InterviewsPilot (InterviewsPilot, 2025). However, there is not an LLM that 
 combines multiple features into an end-to-end, user-friendly application, specifically designed to improve an applicant's chances of successfully 
-completing the job-application cycle. Current LLMs struggle to provide accurate interview preparation based on specific jobs and do not finetune based on
-the user's profile. My interview prep career assistant LLM seeks to provide a full user experience by specifically developing practice job interview questions
+completing the job-application cycle. Current LLMs struggle to provide accurate interview preparation based on specific jobs and are not finetuned based on
+the user's profile. They tend to hallucinate and struggle to include specific user details when developing answers to interview questions, resulting in generic responses.
+Due to these limitations, my interview prep career assistant LLM seeks to provide a full user experience by specifically developing practice job interview questions
 based on the description of the job they are applying for. Additionally, it provides users with an 'optimal' answer to the interview questions based on their
 profile and resume. The interview prep LLM is finetuned from model Qwen2.5-7B-Instruct using LoRA with hyperparameters rank: 64, alpha: 128, and dropout: 0.15. 
-That hyperparameter combination resulted in the lowest validation loss, 2.055938. After finetuning, the LLM performed with a 21.578 in the SqUAdv2 benchmark, a 
-0.597 in the humaneval benchmark, a 5.040 in the E2E NLG Challenge benchmark, and a bert score mean precision of 0.813, mean recall of 0.848, and mean f1 of 0.830
-on a train/test split. 
+That hyperparameter combination resulted in the lowest validation loss, 2.055938. The model was trained on a synthetic dataset that I developed using user job data.
+After finetuning, the LLM performed with a 21.578 in the SQuADv2 benchmark, a 
+0.597 in the humaneval benchmark, a 5.040 bleu score in the E2E NLG Challenge benchmark, and a bert score mean precision of 0.813, mean recall of 0.848, and mean f1 of 0.830
+on a train/test split. The bert scores specifically indicate that my model has a strong alignment between generated and expected responses.
 
 
 ### Model Sources [optional]