activity
20182025
most citedLearning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering

146 citations · 656 across the 31 of their papers we have counts for

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Showing 2022Show all

6 papers · 1 filter

cs.CL2022★ 1 cited

Task-aware Retrieval with Instructions

Akari Asai, Timo Schick, Patrick Lewis +5

We study the problem of retrieval with instructions, where users of a retrieval system explicitly describe their intent along with their queries. We aim to develop a general-purpos…

cs.CL2022★ 34 cited

When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memories

Alex Mallen, Akari Asai, Victor Zhong +3

Despite their impressive performance on diverse tasks, large language models (LMs) still struggle with tasks requiring rich world knowledge, implying the limitations of relying sol…

cs.CL2022

Beyond Counting Datasets: A Survey of Multilingual Dataset Construction and Necessary Resources

Xinyan Velocity Yu, Akari Asai, Trina Chatterjee +2

While the NLP community is generally aware of resource disparities among languages, we lack research that quantifies the extent and types of such disparity. Prior surveys estimatin…

cs.CL2022

MIA 2022 Shared Task: Evaluating Cross-lingual Open-Retrieval Question Answering for 16 Diverse Languages

Akari Asai, Shayne Longpre, Jungo Kasai +6

We present the results of the Workshop on Multilingual Information Access (MIA) 2022 Shared Task, evaluating cross-lingual open-retrieval question answering (QA) systems in 16 typo…

cs.CL2022★ 28 cited

RealTime QA: What's the Answer Right Now?

Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi +7

We introduce REALTIME QA, a dynamic question answering (QA) platform that announces questions and evaluates systems on a regular basis (weekly in this version). REALTIME QA inquire…

cs.CL2022★ 16 cited

ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts

Akari Asai, Mohammadreza Salehi, Matthew E. Peters +1

This work introduces a new multi-task, parameter-efficient language model (LM) tuning method that learns to transfer knowledge across different tasks via a mixture of soft prompts-…