# Application of LLM Agents in Recruitment: A Novel Framework for Resume Screening

arXiv: https://arxiv.org/abs/2401.08315 · HTML: https://mlsanity.com/paper/2401.08315/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 12 of 12)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://mlsanity.com/paper/2111.01243/) (2021)
- [A Survey on Data Augmentation for Text Classification](https://mlsanity.com/paper/2107.03158/) (2021)
- [Introspective Tips: Large Language Model for In-Context Decision Making](https://mlsanity.com/paper/2305.11598/) (2023)
- [JobRecoGPT -- Explainable job recommendations using LLMs](https://mlsanity.com/paper/2309.11805/) (2023)

## Built on by (most cited first, 24 of 24)

- [A Comprehensive Survey on Process-Oriented Automatic Text Summarization with Exploration of LLM-Based Methods](https://mlsanity.com/paper/2403.02901/) (2024)
- [Visibility into AI Agents](https://mlsanity.com/paper/2401.13138/) (2024)
- [Large Language Model Safety: A Holistic Survey](https://mlsanity.com/paper/2412.17686/) (2024)
- [JobFair: A Framework for Benchmarking Gender Hiring Bias in Large Language Models](https://mlsanity.com/paper/2406.15484/) (2024)
- [AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening](https://mlsanity.com/paper/2504.02870/) (2025)
- [LLM Strategic Reasoning: Agentic Study through Behavioral Game Theory](https://mlsanity.com/paper/2502.20432/) (2025)
- [Fairness Definitions in Language Models Explained](https://mlsanity.com/paper/2407.18454/) (2024)
- [Measuring Bias or Measuring the Task: Understanding the Brittle Nature of LLM Gender Biases](https://mlsanity.com/paper/2509.04373/) (2025)
- [Characterizing Bias: Benchmarking Large Language Models in Simplified versus Traditional Chinese](https://mlsanity.com/paper/2505.22645/) (2025)
- [HEARTS: A Holistic Framework for Explainable, Sustainable and Robust Text Stereotype Detection](https://mlsanity.com/paper/2409.11579/) (2024)
- [FAIRE: Assessing Racial and Gender Bias in AI-Driven Resume Evaluations](https://mlsanity.com/paper/2504.01420/) (2025)
- [DICE: A Framework for Dimensional and Contextual Evaluation of Language Models](https://mlsanity.com/paper/2504.10359/) (2025)
- [AI Security Beyond Core Domains: Resume Screening as a Case Study of Adversarial Vulnerabilities in Specialized LLM Applications](https://mlsanity.com/paper/2512.20164/) (2025)
- [Let's Get You Hired: A Job Seeker's Perspective on Multi-Agent Recruitment Systems for Explaining Hiring Decisions](https://mlsanity.com/paper/2505.20312/) (2025)
- [MLAR: Multi-layer Large Language Model-based Robotic Process Automation Applicant Tracking](https://mlsanity.com/paper/2507.10472/) (2025)
- [HR-Agent: A Task-Oriented Dialogue (TOD) LLM Agent Tailored for HR Applications](https://mlsanity.com/paper/2410.11239/) (2024)
- [Bridging Today and the Future of Humanity: AI Safety in 2024 and Beyond](https://mlsanity.com/paper/2410.18114/) (2024)
- [Diversity and Inclusion in AI for Recruitment: Lessons from Industry Workshop](https://mlsanity.com/paper/2411.06066/) (2024)
- [StaffPro: an LLM Agent for Joint Staffing and Profiling](https://mlsanity.com/paper/2507.21636/) (2025)
- [Quantifying Gender Bias in Large Language Models: When ChatGPT Becomes a Hiring Manager](https://mlsanity.com/paper/2604.00011/) (2026)
- [An Interactive Agent for Requirement-Driven Candidate Sourcing](https://mlsanity.com/paper/2608.23501/) (2026)
- [An Efficient Plugin Method for Metric Optimization of Black-Box Models](https://mlsanity.com/paper/2503.02119/) (2025)
- [HALF: Harm-Aware LLM Fairness Evaluation Aligned with Deployment](https://mlsanity.com/paper/2510.12217/) (2025)
- [TalentCLEF at CLEF2026: Skill and Job Title Intelligence for Human Capital Management](https://mlsanity.com/paper/2607.20009/) (2026)

## Abstract (arXiv)

The automation of resume screening is a crucial aspect of the recruitment process in organizations. Automated resume screening systems often encompass a range of natural language processing (NLP) tasks. This paper introduces a novel Large Language Models (LLMs) based agent framework for resume screening, aimed at enhancing efficiency and time management in recruitment processes. Our framework is distinct in its ability to efficiently summarize and grade each resume from a large dataset. Moreover, it utilizes LLM agents for decision-making. To evaluate our framework, we constructed a dataset from actual resumes and simulated a resume screening process. Subsequently, the outcomes of the simulation experiment were compared and subjected to detailed analysis. The results demonstrate that our automated resume screening framework is 11 times faster than traditional manual methods. Furthermore, by fine-tuning the LLMs, we observed a significant improvement in the F1 score, reaching 87.73%, during the resume sentence classification phase. In the resume summarization and grading phase, our fine-tuned model surpassed the baseline performance of the GPT-3.5 model. Analysis of the decision-making efficacy of the LLM agents in the final offer stage further underscores the potential of LLM agents in transforming resume screening processes.