Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval
Authors: Wenhan Xiong, Xiang Li, Srini Iyer, Jingfei Du, Patrick Lewis, William Yang Wang, Yashar Mehdad, Scott Yih, Sebastian Riedel, Douwe Kiela, Barlas Oguz
ICLR 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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, Hotpot QA and multi-evidence FEVER. Our experiments focus on two datasets: Hotpot QA and Multi-evidence FEVER. |
| Researcher Affiliation | Collaboration | 1University of California, Santa Barbara 2University of Massachusetts Amherst Facebook AI University College London EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | Yes | 1https://github.com/facebookresearch/multihop_dense_retrieval. |
| Open Datasets | Yes | Our experiments focus on two datasets: Hotpot QA and Multi-evidence FEVER. Hotpot QA (Yang et al., 2018) includes 113k multi-hop questions... Multi-evidence FEVER includes 20k claims from the FEVER (Thorne et al., 2018) fact verification dataset... |
| Dataset Splits | Yes | Our experiments focus on two datasets: Hotpot QA and Multi-evidence FEVER. Hotpot QA (Yang et al., 2018) includes 113k multi-hop questions... We conduct further analysis on Hotpot QA dev. |
| Hardware Specification | Yes | All the experiments are conducted on a machine with 8 32GB V100 GPUs. |
| Software Dependencies | No | The paper mentions software like 'Huggingface Transformers' and 'FAISS' but does not specify their version numbers, which is required for reproducibility. |
| Experiment Setup | Yes | Table 11: Hyperparameters of Retriever [lists learning rate 2e-5, batch size 150, etc.] Table 12: Hyperparameters of Extractive Reader (ELECTRA) [lists learning rate 5e-5, batch size 128, etc.] |