mlsanityData to 14 Oct 2026

MS MARCO: A Human Generated MAchine Reading COmprehension Dataset

arXiv 1611.09268 ↗First posted 28 Nov 2016cs.CL cs.IRcited by 1,605

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Cited by 294 in Retrieval-augmented generation, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

We introduce a large scale MAchine Reading COmprehension dataset, which we name MS MARCO. The dataset comprises of 1,010,916 anonymized questions---sampled from Bing's search query logs---each with a human generated answer and 182,669 completely human rewritten generated answers. In addition, the dataset contains 8,841,823 passages---extracted from 3,563,535 web documents retrieved by Bing---that provide the information necessary for curating the natural language answers. A question in the MS MARCO dataset may have multiple answers or no answers at all. Using this dataset, we propose three different tasks with varying levels of difficulty: (i) predict if a question is answerable given a set of context passages, and extract and synthesize the answer as a human would (ii) generate a well-formed answer (if possible) based on the context passages that can be understood with the question and passage context, and finally (iii) rank a set of retrieved passages given a question. The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering. We believe that the scale and the real-world nature of this dataset makes it attractive for benchmarking machine reading comprehension and question-answering models.

Key facts. Cited by 1,605 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Nov 2016.

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Citing papers per year: 2020: 1; 2021: 141; 2022: 188; 2023: 239; 2024: 370; 2025: 393; 2026: 273.

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