# Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering

arXiv: https://arxiv.org/abs/1905.05733 · HTML: https://mlsanity.com/paper/1905.05733/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 30 of 48)

- [Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions](https://mlsanity.com/paper/2212.10509/) (2022)
- [Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering](https://mlsanity.com/paper/2101.00774/) (2021)
- [Language Models with Conformal Factuality Guarantees](https://mlsanity.com/paper/2402.10978/) (2024)
- [Case-based Reasoning for Natural Language Queries over Knowledge Bases](https://mlsanity.com/paper/2104.08762/) (2021)
- [Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval](https://mlsanity.com/paper/2101.00436/) (2021)
- [Weakly-Supervised Visual-Retriever-Reader for Knowledge-based Question Answering](https://mlsanity.com/paper/2109.04014/) (2021)
- [BERGEN: A Benchmarking Library for Retrieval-Augmented Generation](https://mlsanity.com/paper/2407.01102/) (2024)
- [Challenges in Generalization in Open Domain Question Answering](https://mlsanity.com/paper/2109.01156/) (2021)
- [Adaptive Information Seeking for Open-Domain Question Answering](https://mlsanity.com/paper/2109.06747/) (2021)
- [Rider: Reader-Guided Passage Reranking for Open-Domain Question Answering](https://mlsanity.com/paper/2101.00294/) (2021)
- [Visconde: Multi-document QA with GPT-3 and Neural Reranking](https://mlsanity.com/paper/2212.09656/) (2022)
- [Database Reasoning Over Text](https://mlsanity.com/paper/2106.01074/) (2021)
- [Boosting Search Engines with Interactive Agents](https://mlsanity.com/paper/2109.00527/) (2021)
- [Rethinking Search: Making Domain Experts out of Dilettantes](https://mlsanity.com/paper/2105.02274/) (2021)
- [Reinforcement Learning from Reformulations in Conversational Question Answering over Knowledge Graphs](https://mlsanity.com/paper/2105.04850/) (2021)
- [Low-Resource Dense Retrieval for Open-Domain Question Answering: A Comprehensive Survey](https://mlsanity.com/paper/2208.03197/) (2022)
- [From Easy to Hard: Two-stage Selector and Reader for Multi-hop Question Answering](https://mlsanity.com/paper/2205.11729/) (2022)
- [Can Open-Domain QA Reader Utilize External Knowledge Efficiently like Humans?](https://mlsanity.com/paper/2211.12707/) (2022)
- [LMSanitator: Defending Prompt-Tuning Against Task-Agnostic Backdoors](https://mlsanity.com/paper/2308.13904/) (2023)
- [Drilling Down into the Discourse Structure with LLMs for Long Document Question Answering](https://mlsanity.com/paper/2311.13565/) (2023)
- [A Graph-guided Multi-round Retrieval Method for Conversational Open-domain Question Answering](https://mlsanity.com/paper/2104.08443/) (2021)
- [Design Challenges for a Multi-Perspective Search Engine](https://mlsanity.com/paper/2112.08357/) (2021)
- [Interactive Question Answering Systems: Literature Review](https://mlsanity.com/paper/2209.01621/) (2022)
- [Understanding and Improving Zero-shot Multi-hop Reasoning in Generative Question Answering](https://mlsanity.com/paper/2210.04234/) (2022)
- [Robust Training for Conversational Question Answering Models with Reinforced Reformulation Generation](https://mlsanity.com/paper/2310.13505/) (2023)
- [1-PAGER: One Pass Answer Generation and Evidence Retrieval](https://mlsanity.com/paper/2310.16568/) (2023)
- [CuriousLLM: Elevating Multi-Document Question Answering with LLM-Enhanced Knowledge Graph Reasoning](https://mlsanity.com/paper/2404.09077/) (2024)
- [ARC: Active and Reflection-driven Context Management for Long-Horizon Information Seeking Agents](https://mlsanity.com/paper/2601.12030/) (2026)
- [ReFusion: Improving Natural Language Understanding with Computation-Efficient Retrieval Representation Fusion](https://mlsanity.com/paper/2401.02993/) (2024)
- [Memory Augmented Sequential Paragraph Retrieval for Multi-hop Question Answering](https://mlsanity.com/paper/2102.03741/) (2021)

## Abstract (arXiv)

This paper introduces a new framework for open-domain question answering in which the retriever and the reader iteratively interact with each other. The framework is agnostic to the architecture of the machine reading model, only requiring access to the token-level hidden representations of the reader. The retriever uses fast nearest neighbor search to scale to corpora containing millions of paragraphs. A gated recurrent unit updates the query at each step conditioned on the state of the reader and the reformulated query is used to re-rank the paragraphs by the retriever. We conduct analysis and show that iterative interaction helps in retrieving informative paragraphs from the corpus. Finally, we show that our multi-step-reasoning framework brings consistent improvement when applied to two widely used reader architectures DrQA and BiDAF on various large open-domain datasets --- TriviaQA-unfiltered, QuasarT, SearchQA, and SQuAD-Open.