---
title: "Who cites DQ-LoRe: Dual Queries with Low — 24 citing papers"
description: "24 papers of the Elamur Research library cite «DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning»…"
canonical: "https://research.elamur.ai/free/en/papers/xiong-2023-dq-lore-dual-queries-low/cited-by/"
---
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# Who cites «DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning»

24 papers of our library cite it (of the 149,439 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning»](https://md.research.elamur.ai/free/en/papers/xiong-2023-dq-lore-dual-queries-low/)

1. [**MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models arXiv:2309.12284**Longhui Yu, Weisen Jiang, Han Shi, et al.*2023 · 565 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-metamath-bootstrap-your-own-mathematical/)
2. **[AutoPSV: Automated Process-Supervised Verifier](https://md.research.elamur.ai/free/en/papers/lu-2024-autopsv-automated-process-supervised-verifier/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jianqiao Lu, Zhiyang Dou, Hongru Wang, et al.*2024 · 28 citations*
3. **[Expression Syntax Information Bottleneck for Math Word Problems](https://md.research.elamur.ai/free/en/papers/xiong-2023-expression-syntax-information-bottleneck-math/) [International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)](https://md.research.elamur.ai/free/en/venues/international-acm-sigir-conference-on-research-and-development-in-information/)**Jing Xiong, Chengming Li, Min Yang, et al.*2023 · 10 citations*

21 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/xiong-2023-dq-lore-dual-queries-low/) — DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning