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20192023
most citedA General Framework for Information Extraction using Dynamic Span Graphs

38 citations · 67 across the 6 of their papers we have counts for

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9 papers · 1 filter

cs.CL20238 cited

Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2

Hamish Ivison, Yizhong Wang, Valentina Pyatkin +8

Since the release of TÜLU [Wang et al., 2023b], open resources for instruction tuning have developed quickly, from better base models to new finetuning techniques. We test and inco…

cs.CL20224 cited

SciFact-Open: Towards open-domain scientific claim verification

David Wadden, Kyle Lo, Bailey Kuehl +4

While research on scientific claim verification has led to the development of powerful systems that appear to approach human performance, these approaches have yet to be tested in…

cs.CL20221 cited

Generating Scientific Claims for Zero-Shot Scientific Fact Checking

Dustin Wright, David Wadden, Kyle Lo +4

Automated scientific fact checking is difficult due to the complexity of scientific language and a lack of significant amounts of training data, as annotation requires domain exper…

cs.CL20213 cited

Overview and Insights from the SciVer Shared Task on Scientific Claim Verification

David Wadden, Kyle Lo

We present an overview of the SciVer shared task, presented at the 2nd Scholarly Document Processing (SDP) workshop at NAACL 2021. In this shared task, systems were provided a scie…

cs.CL202113 cited

Scientific Language Models for Biomedical Knowledge Base Completion: An Empirical Study

Rahul Nadkarni, David Wadden, Iz Beltagy +3

Biomedical knowledge graphs (KGs) hold rich information on entities such as diseases, drugs, and genes. Predicting missing links in these graphs can boost many important applicatio…

cs.CL2020

Extracting a Knowledge Base of Mechanisms from COVID-19 Papers

Tom Hope, Aida Amini, David Wadden +6

The COVID-19 pandemic has spawned a diverse body of scientific literature that is challenging to navigate, stimulating interest in automated tools to help find useful knowledge. We…