Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead
Paper • 2407.00066 • Published
input stringlengths 3.23k 13.3k | output sequencelengths 1 3 | id stringlengths 40 40 |
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Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How do they combine audio and text sequences in their RNN?"
] | task461-d61d08566d954e3d89b16ae192b44239 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How id Depechemood trained?"
] | task461-3d24a0cbbb2e488ba137d8d71e7b17a8 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What were the five English subtasks?"
] | task461-a312f3dc59454f6dab4bf2a6ece285fb |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How are EAC evaluated?"
] | task461-2182a14d5a3f4f3db0d12caa1351aaf6 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How were the navigation instructions collected?"
] | task461-a0cbc347a6b441b4b814b0f39bc0e37a |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What is the baseline for the experiments?"
] | task461-4d6c5b35f8bd467991243fdbc7a7d5f8 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"Which datasets are used?"
] | task461-e82a33ce26f14a25a0befa67b82adda4 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What was the inter-annotator agreement?"
] | task461-8049aee2c02044b2a797e29c559c7bec |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What dataset do they use?",
"What dataset do they use for experiments?"
] | task461-5ac5a884f93d4625840d54c9831fe923 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What are the features of used to customize target user interaction? "
] | task461-164f031f1a3846f2957e06425a416ea3 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What are the tasks used in the mulit-task learning setup?"
] | task461-1fc5545596e44a4bbf6b860fd8892970 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What kind of model do they build to expand abbreviations?"
] | task461-cb0f2d7e6014422f813a383d1a23aac3 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What crowdsourcing platform did they use?"
] | task461-ac5750311b7e465caf2d999814db6678 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"Are results reported only for English data?"
] | task461-d5b9d4a95b9940b2b033df2f833f0d19 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How do this framework facilitate demographic inference from social media?"
] | task461-c3208cc2583b4fea8fd9a0f0a246853c |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How is Relation network used to infer causality at segment level?"
] | task461-fdb94d56ca8b4d3cac95e9dc70936a29 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How do they obtain human judgements?"
] | task461-d53421d4fce84ab78d16c5f502338661 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"Do the authors mention any possible confounds in their study?"
] | task461-0e39f381586440ad9e3377924db25c7d |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What benchmarks are created?"
] | task461-1b2c81c95e144815843c0368c1243850 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What are two models' architectures in proposed solution?"
] | task461-c70adbe8c8b14e8fb2aaa47d222744fe |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What supervised learning tasks are attempted with these representations?"
] | task461-245ffde0f5c34a34b9f594e39dc4b956 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How they use sequence tagging to answer multi-span questions?"
] | task461-95afcc99452d49fdb8d1ed78373adf19 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What datasets did the authors use?"
] | task461-1e3daec1362e41c58bc76d00c8cafd4d |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"Did they experimnet in other languages?"
] | task461-753a832e6e92410e81e390b38b284477 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"what corpus is used to learn behavior?"
] | task461-b705bc909ad24a27b9ddc05a6a8dedbb |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How big is their dataset?"
] | task461-49cf5f9e8b8c447cbbc0efd86d366fc9 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What codemixed language pairs are evaluated?"
] | task461-5c7afb8d19724d8fb5159836c40fc83c |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What's the size of the previous largest OpenIE dataset?"
] | task461-0b018e7bd2d64a00b5c0ab5f25654f40 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"what datasets were used?"
] | task461-ccf000947d6c49f6a2536a46f929b5b0 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How many annotators were there?"
] | task461-6f53595f6e914e4fa9efd43cde6d69fd |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What are the languages they consider in this paper?"
] | task461-a62a385fbef84abeae4bc510184b611a |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"Which NLP area have the highest average citation for woman author?"
] | task461-b4768156d85f4828982139704bc113e2 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How better does HAKE model peform than state-of-the-art methods?"
] | task461-67906f68b0624c408d20ced86108951d |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"Who is the crowd in these experiments?"
] | task461-6a180bea2cd24835afceae1abeeb3eec |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What are the existing biases?"
] | task461-21a637bbd4734ba8bb85c0d28151abae |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What intents does the paper explore?"
] | task461-c0a999829b9a41f2844c6318b1d59265 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How is the accuracy of the system measured?"
] | task461-3bbe12a416ca4d7fbab4982a0e5b4591 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How is the sequential nature of the story captured?"
] | task461-71bacd424e8741cbac0ba2673f4847db |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"Do they release MED?"
] | task461-e06265b4797e429faa1643bd9f86c52c |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"Which retrieval system was used for baselines?"
] | task461-4d7ca357f75a4399a149ae8f9f938324 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How they model style as a suite of low-level linguistic controls, such as frequency of pronouns, prepositions, and subordinate clause constructions?"
] | task461-ba4202ccd84d4f48be2d419ff5f80a40 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How do they demonstrate the robustness of their results?"
] | task461-be6e99f3386e4129b095b85d67721610 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What visual information characterizes tones?"
] | task461-82cdab0252c94ee3a515385a4ff7f16b |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How do the authors define or exemplify 'incorrect words'?"
] | task461-8380121337b240b18f465e4a3684c0e5 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How does their perturbation algorihm work?"
] | task461-1b97fe1586334a56a2bc1bef86199443 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What word-based and dictionary-based feature are used?"
] | task461-0aac4e7c37574a53b8fd6a5ebc280980 |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"Do the authors mention any confounds to their study?"
] | task461-6e5f95fdd93e4090aa180f7e8f9babef |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"How do they score phrasal compositionality?"
] | task461-cabc759167d5490d85e5cd1a52c4aa4f |
Definition: In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.
Positive Example 1 -
Input: Questions are gathered from anonymized, aggregated queries to the Goog... | [
"What architectures are explored to improve the seq2seq model?"
] | task461-f747bc5a1e5d44a0b99fd32b47ee50ca |
End of preview. Expand in Data Studio
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task461_qasper_question_generation
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks},
author={Yizhong Wang and Swaroop Mishra and Pegah Alipoormolabashi and Yeganeh Kordi and Amirreza Mirzaei and Anjana Arunkumar and Arjun Ashok and Arut Selvan Dhanasekaran and Atharva Naik and David Stap and Eshaan Pathak and Giannis Karamanolakis and Haizhi Gary Lai and Ishan Purohit and Ishani Mondal and Jacob Anderson and Kirby Kuznia and Krima Doshi and Maitreya Patel and Kuntal Kumar Pal and Mehrad Moradshahi and Mihir Parmar and Mirali Purohit and Neeraj Varshney and Phani Rohitha Kaza and Pulkit Verma and Ravsehaj Singh Puri and Rushang Karia and Shailaja Keyur Sampat and Savan Doshi and Siddhartha Mishra and Sujan Reddy and Sumanta Patro and Tanay Dixit and Xudong Shen and Chitta Baral and Yejin Choi and Noah A. Smith and Hannaneh Hajishirzi and Daniel Khashabi},
year={2022},
eprint={2204.07705},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2204.07705},
}
More details can also be found in the following paper:
@misc{brüelgabrielsson2024compressserveservingthousands,
title={Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead},
author={Rickard Brüel-Gabrielsson and Jiacheng Zhu and Onkar Bhardwaj and Leshem Choshen and Kristjan Greenewald and Mikhail Yurochkin and Justin Solomon},
year={2024},
eprint={2407.00066},
archivePrefix={arXiv},
primaryClass={cs.DC},
url={https://arxiv.org/abs/2407.00066},
}
Contact Information
For any comments or questions, please email Rickard Brüel Gabrielsson
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Homepage:
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Paper:
arxiv.org
Point of Contact:
Rickard Brüel Gabrielsson
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