{"id":186622,"date":"2025-05-05T10:20:42","date_gmt":"2025-05-05T14:20:42","guid":{"rendered":"https:\/\/www.hajim.rochester.edu\/senior-design-day\/?p=186622"},"modified":"2026-05-04T08:43:41","modified_gmt":"2026-05-04T12:43:41","slug":"circlestar%ef%bd%9chow-to-make-your-resume-fool-llm","status":"publish","type":"post","link":"https:\/\/www.hajim.rochester.edu\/senior-design-day\/circlestar%ef%bd%9chow-to-make-your-resume-fool-llm\/","title":{"rendered":"CircleStar\uff5cHow to Make Your Resume Fool LLM"},"content":{"rendered":"\n<div class=\"wp-block-group\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h2 class=\"wp-block-heading\">Contributors<\/h2>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Sponsor: Valerie Carey (circlestar)<\/li>\n\n\n\n<li>Instructor: Cantay Caliskan<\/li>\n\n\n\n<li>Qike Jiang, Yuewen Yan, Yang Zhang, Mark(Ke) Xu<\/li>\n\n\n\n<li>Goergen Institute for Data Science and Artificial Intelligence<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Abstract<\/h2>\n<\/div><\/div>\n\n\n\n<p>As AI tools enter hiring, understanding how they interpret resumes is critical. We studied how machine learning models and LLM APIs classify resumes and infer experience, using 2,484 samples across 24 job categories. Models like DistilBERT and ChatGPT-4.1 performed well on clear roles but struggled with overlapping fields and sometimes produced invalid labels. Our findings reveal key biases and offer insights for improving AI-driven hiring.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Introduction<\/h2>\n\n\n\n<p>Advancements in Large Language Models (LLMs) have revolutionized HR recruitment, offering rapid and efficient resume screening compared to traditional methods [2]. However, how LLMs interpret resumes and determine candidate suitability remains unclear.<\/p>\n\n\n\n<p>This study explores how LLMs interact with resume data and identifies crucial factors influencing their decisions. We also examine effective strategies for job seekers to tailor resumes, optimizing their chances to stand out and avoid mismatches in automated screening.<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-layout-flow wp-block-quote-is-layout-flow\">\n<p>\u201cSometimes the knowledge we really need isn\u2019t contained in the questions we know to ask.\u201d \u2014 Valerie Carey<\/p>\n<\/blockquote>\n\n\n\n<h2 class=\"wp-block-heading\">Data &amp; Exploratory Analysis<\/h2>\n\n\n\n<p>Sourced from Kaggle and compiled by Snehaan Bhawal (2021), the dataset contains 2,484 resumes grouped into 24 job categories [1]. Each sample includes a unique ID, the raw text version, an HTML-structured version, the ground truth label, and the predicted category.<\/p>\n\n\n\n<p>The pie chart represents the distribution of resume categories in our dataset. In histogram, we see that most resumes have 500\u20131,000 words, with a peak around 700 words; a few outliers exceed 2,000 words.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"787\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-7-copy-1-1024x787.png\" alt=\"\" class=\"wp-image-202092\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-7-copy-1-1024x787.png 1024w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-7-copy-1-300x231.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-7-copy-1-768x590.png 768w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-7-copy-1.png 1068w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"850\" height=\"545\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-2.png\" alt=\"\" class=\"wp-image-202072\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-2.png 850w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-2-300x192.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-2-768x492.png 768w\" sizes=\"auto, (max-width: 850px) 100vw, 850px\" \/><\/figure>\n\n\n\n<p>As part of our exploratory data analysis, we trained an XGBoost model to serve as a benchmark for evaluating the accuracy of LLM predictions across different categories.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Methods<\/h2>\n\n\n\n<p><strong>Resume Category Classification &amp; Evaluation<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>We used three large-language models (GPT-4o, GPT-4.1, and DeepSeek V3, distillBERT) to assign each resume to one of 24 predefined job-function categories (e.g., HR, Finance, Engineering). Each resume\u2019s \u201cResume_str\u201d text was sent to the models, the single-word prediction capture.<\/li>\n\n\n\n<li>Accuracy, cost are parallel compared. The result is then visualized as a heat graph, the misclassification by LLM will be carefully further evaluated.<\/li>\n<\/ul>\n\n\n\n<p><strong>Experience Estimation and Level Classification<\/strong><\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>We used GPT-4o to estimate total years of experience and career level (Entry, Mid, Senior, Executive) for each resume.&nbsp;<\/li>\n\n\n\n<li>A standardized prompt generated structured JSON outputs, enabling automated labeling across 2,484 resumes.&nbsp;<\/li>\n\n\n\n<li>These results supported our analysis of experience distributions, career ranges, and dominant keywords by level.<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Results<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\">Result &#8211; Resume Category Classification &amp; Evaluation<\/h3>\n\n\n\n<p>Most classification shows reliable, however all three LLMs showing very similar misclassification on certain type of jobs:<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1000\" height=\"800\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/35701746315825_.pic_.png\" alt=\"\" class=\"wp-image-205482\" style=\"width:736px;height:auto\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/35701746315825_.pic_.png 1000w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/35701746315825_.pic_-300x240.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/35701746315825_.pic_-768x614.png 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n<\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"1000\" height=\"800\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/35691746315824_.pic_.png\" alt=\"\" class=\"wp-image-205492\" style=\"width:750px;height:auto\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/35691746315824_.pic_.png 1000w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/35691746315824_.pic_-300x240.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/35691746315824_.pic_-768x614.png 768w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/figure>\n<\/div>\n\n\n<p>The few categories that the model frequently mis-classifies all belong to industries whose resumes share overlapping similar words and role descriptions; the classifier therefore \u201cslides\u201d them into better-represented neighbours that reuse the same vocabulary. A prime example is BPO, whose job ads emphasise client management and revenue growth, the same phrases that dominate Business-Development resumes, so nearly half of the true BPO cases are predicted as Business-Development.<br>Similar term-sharing drives the smaller leaks we see between:<br>Banking \u2192 Finance, Arts \u2192 Designer, and Apparel \u2192 Public-Relations.<\/p>\n\n\n\n<p>It is quite surprising to see that all the LLMs generated several predicted categories that were not included in the prompt, as shown in the table. ChatGPT-4.1 and DeepSeek-V3 demonstrated similarly better performance compared to others. However, considering the cost of running long resume data, DeepSeek-V3 stands out as a much more cost-effective option!<\/p>\n\n\n\n<p>If we also consider fine-tuning LLMs with an additional prediction layer, it can effectively prevent the appearance of invalid predicted categories and achieve higher accuracy, with very little cost (since it runs locally \u2014 about 5 minutes of training on an RTX 3060). However, it requires labeled training data and is harder to extend to different job categories. Additionally, we are limited by the BERT model\u2019s 512-token constraint, which means that more than half of each resume is cut off.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Result &#8211; Experience Estimation and Level Classification<\/h3>\n\n\n\n<p>Experience Years Distribution<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Entry-level<\/strong>: candidates are tightly clustered between 0\u20135 years, showing a sharp early peak.<\/li>\n\n\n\n<li><strong>Mid-level<\/strong>: candidates center around 5\u201312 years, with a broader, right-skewed distribution.<\/li>\n\n\n\n<li><strong>Senior-level<\/strong>: professionals appear between 12\u201325 years, with moderate density.<\/li>\n\n\n\n<li><strong>Executive-level<\/strong>: roles stretch across 20\u201340+ years, showing a flatter and wider distribution.<br>\u2794 LLM-based annotations correspond meaningfully to expected career experiences.<br>\u2794 Outliers were observed in each experience level group. This may be due to limitations of the LLM in interpreting phrases such as &#8220;to present&#8221; or &#8220;current,&#8221; where no explicit end year was provided.<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"984\" height=\"584\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-16-1.png\" alt=\"\" class=\"wp-image-202162\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-16-1.png 984w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-16-1-300x178.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-16-1-768x456.png 768w\" sizes=\"auto, (max-width: 984px) 100vw, 984px\" \/><\/figure>\n\n\n\n<p>Dominant Words by Career Level<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>The top words extracted for each level reflect typical responsibilities and contexts associated with that career stage:\n<ul class=\"wp-block-list\">\n<li><strong>Entry-level<\/strong>: Emphasizes early roles, basic skills, and transitional positions requiring limited experience.<\/li>\n\n\n\n<li><strong>Mid-level<\/strong>: Suggests operational duties, customer interaction, and individual contributions typical of developing professionals.<\/li>\n\n\n\n<li><strong>Senior-level<\/strong>: Highlights management, oversight, and complex deliverables aligned with leadership positions.<\/li>\n\n\n\n<li><strong>Executive<\/strong>: Reflects strategic leadership, financial scale, and vision-setting responsibilities.<br>\u2794 The dominant keywords offer semantic evidence that the LLM effectively captured contextual cues from the resumes, enriching the career level classification.<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\n<figure class=\"wp-block-image size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"989\" height=\"390\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-14.png\" alt=\"\" class=\"wp-image-202172\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-14.png 989w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-14-300x118.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/download-14-768x303.png 768w\" sizes=\"auto, (max-width: 989px) 100vw, 989px\" \/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\">References<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li>Bhawal, S. (2021, August 8). Resume dataset. Kaggle. <a href=\"https:\/\/www.kaggle.com\/datasets\/snehaanbhawal\/resume-dataset\">https:\/\/www.kaggle.com\/datasets\/snehaanbhawal\/resume-dataset<\/a>&nbsp;&nbsp;<\/li>\n\n\n\n<li>C. Gan, Q. Zhang, and T. Mori, \u201cApplication of LLM agents in recruitment: A novel framework for resume screening,\u201d arXiv (Cornell University), Aug. 2024, <a href=\"https:\/\/arxiv.org\/abs\/2401.08315\">https:\/\/arxiv.org\/abs\/2401.08315<\/a>.&nbsp;&nbsp;&nbsp;&nbsp;<\/li>\n\n\n\n<li>S. S. Helli, S. Tanberk, and S. N. Cavsak, \u201cResume information extraction via Post-OCR text processing,\u201d arXiv (Cornell University), Jan. 2023, doi: 10.48550\/arxiv.2306.13775.\u00a0\u00a0\u00a0<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">Acknowledgements<\/h2>\n\n\n\n<p>We would like to sincerely thank our sponsor Valerie Carey, Professor Caliskan, Professor Anand, and the Georgen Institute for Data Science &amp; AI for their support and guidance throughout this project.<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"270\" height=\"81\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/logo_hi_white_small.png\" alt=\"\" class=\"wp-image-202242\" style=\"width:200px\"\/><\/figure>\n<\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"alignleft size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"976\" height=\"256\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/Screenshot-2025-05-02-at-8.18.14\u202fPM.png\" alt=\"\" class=\"wp-image-202232\" style=\"width:400px\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/Screenshot-2025-05-02-at-8.18.14\u202fPM.png 976w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/Screenshot-2025-05-02-at-8.18.14\u202fPM-300x79.png 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/Screenshot-2025-05-02-at-8.18.14\u202fPM-768x201.png 768w\" sizes=\"auto, (max-width: 976px) 100vw, 976px\" \/><\/figure>\n<\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright size-full is-resized\"><img loading=\"lazy\" decoding=\"async\" width=\"986\" height=\"203\" src=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/4618-GIDS-Wordmark-V2-FINAL.jpg\" alt=\"\" class=\"wp-image-202252\" style=\"width:400px\" srcset=\"https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/4618-GIDS-Wordmark-V2-FINAL.jpg 986w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/4618-GIDS-Wordmark-V2-FINAL-300x62.jpg 300w, https:\/\/www.hajim.rochester.edu\/senior-design-day\/wp-content\/uploads\/2025\/05\/4618-GIDS-Wordmark-V2-FINAL-768x158.jpg 768w\" sizes=\"auto, (max-width: 986px) 100vw, 986px\" \/><\/figure>\n<\/div>\n\n\n<p><\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Contributors Abstract As AI tools enter hiring, understanding how they interpret resumes is critical. We studied how machine learning models and LLM APIs classify resumes and infer experience, using 2,484&hellip;<\/p>\n","protected":false},"author":15802,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[4442,106],"tags":[],"coauthors":[20192,20262,20462,20222],"class_list":["post-186622","post","type-post","status-publish","format-standard","hentry","category-archive","category-dsc-archive"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>CircleStar\uff5cHow to Make Your Resume Fool LLM - Senior Design Day<\/title>\n<meta name=\"description\" content=\"We studied how machine learning models and LLM APIs classify resumes and infer experience, using 2,484 samples across 24 job categories. 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