Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of Retrieval Augmentation to Language Models
This repository is the codebase of our paper "Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of Retrieval Augmentation to Language Models".
@inproceedings{maekawa2024retrieval,
title = "Retrieval Helps or Hurts? A Deeper Dive into the Efficacy of Retrieval Augmentation to Language Models",
author = "Seiji Maekawa and Hayate Iso and Sairam Gurajada and Nikita Bhutani",
year = "2024",
booktitle= "Proceedings of the 2024 Annual Conference of the North American Chapter of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2402.13492",
}| Description | Count |
|---|---|
| Questions | 14,837 |
| Unique subject entities | 13,251 |
| Unique object entities | 7,642 |
| Average length of supporting passages (characters) | 214.3 |
| Questions added in first roundtrip | 12,856 |
| Questions added in second roundtrip | 823 |
| Questions added in third roundtrip | 283 |
| Questions written by annotators | 743 |
| ID | Dataset | Copyright Holder | Source Link | License |
|---|---|---|---|---|
| 1 | WiTQA | Megagon Labs | Wikipedia dump, OpenAI API | CC BY-SA 4.0 license |
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