Papers › Atlas: Few-shot Learning with Retrieval Augmented Language Models
Atlas: Few-shot Learning with Retrieval Augmented Language Models
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, Edouard Grave
Large language models have shown impressive few-shot results on a wide range of tasks. However, when knowledge is key for such results, as is the case for tasks such as question answering and fact checking, massive parameter counts to store knowledge seem to be needed. Retrieval augmented models are known to excel at knowledge intensive tasks without the need for as many parameters, but it is unclear whether they work in few-shot settings. In this work we present Atlas, a carefully designed and pre-trained retrieval augmented language model able to learn knowledge intensive tasks with very few training examples. We perform evaluations on a wide range of tasks, including MMLU, KILT and NaturalQuestions, and study the impact of the content of the document index, showing that it can easily be updated. Notably, Atlas reaches over 42% accuracy on Natural Questions using only 64 examples, outperforming a 540B parameters model by 3% despite having 50x fewer parameters.
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Multi-task Language Understanding | MML | Atlas (5-shot) | Average (%) | 47.9 | #26 of 44 | Archive leaderboard | report |
| Question Answering | Natural Questions | Atlas (full, Wiki-dec-2018 index) | EM | 64.0 | #1 of 47 | Archive leaderboard | report |
| Question Answering | Natural Questions | Atlas (full, Wiki-dec-2021+CC index) | EM | 60.4 | #2 of 47 | Archive leaderboard | report |
| Question Answering | Natural Questions | Atlas (few-shot, k=64, Wiki-Dec-2018 index) | EM | 45.1 | #17 of 47 | Archive leaderboard | report |
| Question Answering | Natural Questions | Atlas (few-shot, k=64, Wiki-dec-2021+CC index) | EM | 42.4 | #22 of 47 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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