# Query Understanding via Intent Description Generation

arXiv: https://arxiv.org/abs/2008.10889 · HTML: https://mlsanity.com/paper/2008.10889/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Aug 2020.

## Built on by (most cited first, 8 of 8)

- [QUILL: Query Intent with Large Language Models using Retrieval Augmentation and Multi-stage Distillation](https://mlsanity.com/paper/2210.15718/) (2022)
- [Event-Centric Query Expansion in Web Search](https://mlsanity.com/paper/2305.19019/) (2023)
- [Powering Job Search at Scale: LLM-Enhanced Query Understanding in Job Matching Systems](https://mlsanity.com/paper/2509.09690/) (2025)
- [CREST: Improving Interpretability and Effectiveness of Troubleshooting at Ericsson through Criterion-Specific Trouble Report Retrieval](https://mlsanity.com/paper/2511.17417/) (2025)
- [QUARK: Robust Retrieval under Non-Faithful Queries via Query-Anchored Aggregation](https://mlsanity.com/paper/2601.21049/) (2026)
- [Sensitivity-Aware Retrieval-Augmented Intent Clarification](https://mlsanity.com/paper/2603.06025/) (2026)
- [Unified Semantic Modeling Framework for Large-Scale Job Understanding at LinkedIn](https://mlsanity.com/paper/2607.24783/) (2026)
- [CG-Probes: Recovering Guardrail Directions from Patient Query Embeddings](https://mlsanity.com/paper/2609.31062/) (2026)

## Abstract (arXiv)

Query understanding is a fundamental problem in information retrieval (IR), which has attracted continuous attention through the past decades. Many different tasks have been proposed for understanding users' search queries, e.g., query classification or query clustering. However, it is not that precise to understand a search query at the intent class/cluster level due to the loss of many detailed information. As we may find in many benchmark datasets, e.g., TREC and SemEval, queries are often associated with a detailed description provided by human annotators which clearly describes its intent to help evaluate the relevance of the documents. If a system could automatically generate a detailed and precise intent description for a search query, like human annotators, that would indicate much better query understanding has been achieved. In this paper, therefore, we propose a novel Query-to-Intent-Description (Q2ID) task for query understanding. Unlike those existing ranking tasks which leverage the query and its description to compute the relevance of documents, Q2ID is a reverse task which aims to generate a natural language intent description based on both relevant and irrelevant documents of a given query. To address this new task, we propose a novel Contrastive Generation model, namely CtrsGen for short, to generate the intent description by contrasting the relevant documents with the irrelevant documents given a query. We demonstrate the effectiveness of our model by comparing with several state-of-the-art generation models on the Q2ID task. We discuss the potential usage of such Q2ID technique through an example application.