Papers
Topics
Authors
Recent
Detailed Answer
Quick Answer
Concise responses based on abstracts only
Detailed Answer
Well-researched responses based on abstracts and relevant paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses
Gemini 2.5 Flash
Gemini 2.5 Flash 87 tok/s
Gemini 2.5 Pro 45 tok/s Pro
GPT-5 Medium 32 tok/s Pro
GPT-5 High 29 tok/s Pro
GPT-4o 105 tok/s Pro
Kimi K2 202 tok/s Pro
GPT OSS 120B 461 tok/s Pro
Claude Sonnet 4 38 tok/s Pro
2000 character limit reached

Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval (2009.12756v2)

Published 27 Sep 2020 in cs.CL

Abstract: We propose a simple and efficient multi-hop dense retrieval approach for answering complex open-domain questions, which achieves state-of-the-art performance on two multi-hop datasets, HotpotQA and multi-evidence FEVER. Contrary to previous work, our method does not require access to any corpus-specific information, such as inter-document hyperlinks or human-annotated entity markers, and can be applied to any unstructured text corpus. Our system also yields a much better efficiency-accuracy trade-off, matching the best published accuracy on HotpotQA while being 10 times faster at inference time.

Citations (175)
List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.

Summary

We haven't generated a summary for this paper yet.

Dice Question Streamline Icon: https://streamlinehq.com

Follow-Up Questions

We haven't generated follow-up questions for this paper yet.

Youtube Logo Streamline Icon: https://streamlinehq.com