# Context Aware Query Rewriting for Text Rankers using LLM

**Authors:** Abhijit Anand, Venktesh V, Vinay Setty, Avishek Anand  
**arXiv:** [2308.16753](https://arxiv.org/abs/2308.16753)  
**Date:** 2023-08-31  
**Summary page:** https://www.summarizepaper.com/en/paper/2308.16753v1/

## Key points

- Comprehensive framework for leveraging large-language models (LLMs) to improve document ranking through query rewriting
- Proposed approach called context-aware query rewriting (CAR)
- CAR offers significant improvements in passage and document ranking tasks compared to using original queries
- Importance of considering surrounding context in paraphrasing
- Challenges associated with developing the CAR framework
- More principled approach for identifying and filtering ambiguous queries suggested
- Mention of other related approaches in query rewriting, such as statistical methods and generative models
- Discussion on recent advancements in natural language question generation for query reformulation
- Relevance feedback methods used in e-commerce domains mentioned
- Experimental results demonstrate effectiveness of the CAR framework in improving retrieval performance
- Potential of LLMs for improved document ranking highlighted

## Summary

This work presents a comprehensive framework for leveraging large-language models (LLMs) to improve document ranking through query rewriting. The proposed approach, called context-aware query rewriting (CAR), addresses limitations of using LLMs as query re-writers and offers significant improvements in passage and document ranking tasks compared to using original queries. The authors also discuss the importance of considering surrounding context in paraphrasing and highlight challenges associated with developing their framework. They suggest a more principled approach for identifying and filtering ambiguous queries and mention other related approaches in query rewriting, such as statistical methods and generative models. Additionally, they discuss recent advancements in natural language question generation for query reformulation and relevance feedback methods used in e-commerce domains. Experimental results demonstrate the effectiveness of the CAR framework in improving downstream retrieval performance. Overall, this work highlights the potential of LLMs for improved document ranking and presents a promising solution with its CAR approach.