# Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems

arXiv: https://arxiv.org/abs/2509.09690 · HTML: https://mlsanity.com/paper/2509.09690/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 19 Aug 2025.

## Builds on (most cited first, 18 of 18)

- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://mlsanity.com/paper/2305.18290/) (2023)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://mlsanity.com/paper/2110.08207/) (2021)
- [An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning](https://mlsanity.com/paper/2308.08747/) (2023)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [RecMind: Large Language Model Powered Agent For Recommendation](https://mlsanity.com/paper/2308.14296/) (2023)
- [MACRec: a Multi-Agent Collaboration Framework for Recommendation](https://mlsanity.com/paper/2402.15235/) (2024)
- [On the Factory Floor: ML Engineering for Industrial-Scale Ads Recommendation Models](https://mlsanity.com/paper/2209.05310/) (2022)
- [Aligning Query Representation with Rewritten Query and Relevance Judgments in Conversational Search](https://mlsanity.com/paper/2407.20189/) (2024)
- [LLM-based Query Expansion Fails for Unfamiliar and Ambiguous Queries](https://mlsanity.com/paper/2505.12694/) (2025)
- [Query Understanding via Intent Description Generation](https://mlsanity.com/paper/2008.10889/) (2020)
- [Exploring Query Understanding for Amazon Product Search](https://mlsanity.com/paper/2408.02215/) (2024)
- [LinkSAGE: Optimizing Job Matching Using Graph Neural Networks](https://mlsanity.com/paper/2402.13430/) (2024)
- [DeepTagger: Knowledge Enhanced Named Entity Recognition for Web-Based Ads Queries](https://mlsanity.com/paper/2306.17413/) (2023)
- [A Scalable and Efficient Signal Integration System for Job Matching](https://mlsanity.com/paper/2507.09797/) (2025)
- [Enhancing CTR Prediction in Recommendation Domain with Search Query Representation](https://mlsanity.com/paper/2410.21487/) (2024)

## Built on by (most cited first, 2 of 2)

- [Policy-Grounded Dynamic Facet Suggestions for Job Search](https://mlsanity.com/paper/2605.16479/) (2026)
- [A Unified Structured Query Understanding Framework for Industrial Semantic Search](https://mlsanity.com/paper/2605.27441/) (2026)

## Abstract (arXiv)

Query understanding is essential in modern relevance systems, where user queries are often short, ambiguous, and highly context-dependent. Traditional approaches often rely on multiple task-specific Named Entity Recognition models to extract structured facets as seen in job search applications. However, this fragmented architecture is brittle, expensive to maintain, and slow to adapt to evolving taxonomies and language patterns. In this paper, we introduce a unified query understanding framework powered by a Large Language Model (LLM), designed to address these limitations. Our approach jointly models the user query and contextual signals such as profile attributes to generate structured interpretations that drive more accurate and personalized recommendations. The framework improves relevance quality in online A/B testing while significantly reducing system complexity and operational overhead. The results demonstrate that our solution provides a scalable and adaptable foundation for query understanding in dynamic web applications.