# The ReQAP System for Question Answering over Personal Information

arXiv: https://arxiv.org/abs/2508.06880 · HTML: https://mlsanity.com/paper/2508.06880/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 9 Aug 2025.

## Builds on (most cited first, 11 of 11)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction](https://mlsanity.com/paper/2304.11015/) (2023)
- [TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data](https://mlsanity.com/paper/2005.08314/) (2020)
- [Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation](https://mlsanity.com/paper/2308.15363/) (2023)
- [SPLADE: Sparse Lexical and Expansion Model for First Stage Ranking](https://mlsanity.com/paper/2107.05720/) (2021)
- [The Dawn of Natural Language to SQL: Are We Fully Ready?](https://mlsanity.com/paper/2406.01265/) (2024)
- [SPAGHETTI: Open-Domain Question Answering from Heterogeneous Data Sources with Retrieval and Semantic Parsing](https://mlsanity.com/paper/2406.00562/) (2024)
- [TimelineQA: A Benchmark for Question Answering over Timelines](https://mlsanity.com/paper/2306.01069/) (2023)
- [Explainable Conversational Question Answering over Heterogeneous Sources via Iterative Graph Neural Networks](https://mlsanity.com/paper/2305.01548/) (2023)
- [Recursive Question Understanding for Complex Question Answering over Heterogeneous Personal Data](https://mlsanity.com/paper/2505.11900/) (2025)

## Built on by (most cited first, 1 of 1)

- [PerCache: Predictive Hierarchical Cache for RAG Applications on Mobile Devices](https://mlsanity.com/paper/2601.11553/) (2025)

## Abstract (arXiv)

Personal information is abundant on users' devices, from structured data in calendar, shopping records or fitness tools, to unstructured contents in mail and social media posts. This works presents the ReQAP system that supports users with answers for complex questions that involve filters, joins and aggregation over heterogeneous sources. The unique trait of ReQAP is that it recursively decomposes questions and incrementally builds an operator tree for execution. Both the question interpretation and the individual operators make smart use of light-weight language models, with judicious fine-tuning. The demo showcases the rich functionality for advanced user questions, and also offers detailed tracking of how the answers are computed by the operators in the execution tree. Being able to trace answers back to the underlying sources is vital for human comprehensibility and user trust in the system.