Title: Simple Linear Attention Language Models Balance The Recall-Throughput Tradeoff
A Variant of the SQuAD question answering task, as implemented by Based. See [https://github.com/EleutherAI/lm-evaluation-harness/lm_eval/tasks/squadv2/README.md] for more info.
Homepage: https://github.com/HazyResearch/based-evaluation-harness
@misc{arora2024simple,
title={Simple linear attention language models balance the recall-throughput tradeoff},
author={Simran Arora and Sabri Eyuboglu and Michael Zhang and Aman Timalsina and Silas Alberti and Dylan Zinsley and James Zou and Atri Rudra and Christopher Ré},
year={2024},
eprint={2402.18668},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{rajpurkar2018know,
title={Know What You Don't Know: Unanswerable Questions for SQuAD},
author={Pranav Rajpurkar and Robin Jia and Percy Liang},
year={2018},
eprint={1806.03822},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
squad_completion
: the SQuAD task as implemented in the paper "Simple linear attention language models balance the recall-throughput tradeoff". Designed for zero-shot evaluation of small LMs.
For adding novel benchmarks/datasets to the library:
- Is the task an existing benchmark in the literature?
- Have you referenced the original paper that introduced the task?
- If yes, does the original paper provide a reference implementation? If so, have you checked against the reference implementation and documented how to run such a test?
If other tasks on this dataset are already supported:
- Is the "Main" variant of this task clearly denoted?
- Have you provided a short sentence in a README on what each new variant adds / evaluates?
- Have you noted which, if any, published evaluation setups are matched by this variant?