# Beyond the Resum'e: A Rubric-Aware Automatic Interview System for Information Elicitation

arXiv: https://arxiv.org/abs/2603.01775 · HTML: https://mlsanity.com/paper/2603.01775/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 33 references that are in the map.
- First posted on arXiv 2 Mar 2026.

## Builds on (most cited first, 13 of 13)

- [BED-LLM: Intelligent Information Gathering with LLMs and Bayesian Experimental Design](https://mlsanity.com/paper/2508.21184/) (2025)
- [METAL: Metamorphic Testing Framework for Analyzing Large-Language Model Qualities](https://mlsanity.com/paper/2312.06056/) (2023)
- [AI Hiring with LLMs: A Context-Aware and Explainable Multi-Agent Framework for Resume Screening](https://mlsanity.com/paper/2504.02870/) (2025)
- [Mitigating the Bias of Large Language Model Evaluation](https://mlsanity.com/paper/2409.16788/) (2024)
- [Competence-Level Prediction and Resume & Job Description Matching Using Context-Aware Transformer Models](https://mlsanity.com/paper/2011.02998/) (2020)
- [Investigating the Vulnerability of LLM-as-a-Judge Architectures to Prompt-Injection Attacks](https://mlsanity.com/paper/2505.13348/) (2025)
- [Can You Trick the Grader? Adversarial Persuasion of LLM Judges](https://mlsanity.com/paper/2508.07805/) (2025)
- [PersonalityChat: Conversation Distillation for Personalized Dialog Modeling with Facts and Traits](https://mlsanity.com/paper/2401.07363/) (2024)
- [Does the Prompt-based Large Language Model Recognize Students' Demographics and Introduce Bias in Essay Scoring?](https://mlsanity.com/paper/2504.21330/) (2025)
- ["Finding the Magic Sauce": Exploring Perspectives of Recruiters and Job Seekers on Recruitment Bias and Automated Tools](https://mlsanity.com/paper/2301.11958/) (2023)
- [A Career Interview Dialogue System using Large Language Model-based Dynamic Slot Generation](https://mlsanity.com/paper/2412.16943/) (2024)
- [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)
- [Modular AI-Powered Interviewer with Dynamic Question Generation and Expertise Profiling](https://mlsanity.com/paper/2601.11534/) (2025)

## Abstract (arXiv)

Effective hiring is integral to the success of an organisation, but it is very challenging to find the most suitable candidates because expert evaluation (e.g. interviews conducted by a technical manager) are expensive to deploy at scale. Therefore, automated resume scoring and other applicant-screening methods are increasingly used to coarsely filter candidates, making decisions on limited information. We propose that large language models (LLMs) can play the role of subject matter experts to cost-effectively elicit information from each candidate that is nuanced and role-specific, thereby improving the quality of early-stage hiring decisions. We present a system that leverages an LLM interviewer to update belief over an applicant's rubric-oriented latent traits in a calibrated way. We evaluate our system on simulated interviews and show that belief converges towards the simulated applicants' artificially-constructed latent ability levels. We release code, a modest dataset of public-domain/anonymised resumes, belief calibration tests, and simulated interviews, at hrefhttps://github.com/mbzuai-nlp/beyond-the-resumehttps://github.com/mbzuai-nlp/beyond-the-resume. Our demo is available at hrefhttps://btr.hstu.nethttps://btr.hstu.net.