Search Results for author: David Wadden

Found 14 papers, 11 papers with code

Estimating the Causal Effect of Early ArXiving on Paper Acceptance

1 code implementation24 Jun 2023 Yanai Elazar, Jiayao Zhang, David Wadden, Bo Zhang, Noah A. Smith

However, since quality is a challenging construct to estimate, we use the negative outcome control method, using paper citation count as a control variable to debias the quality confounding effect.

Causal Inference

How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources

1 code implementation7 Jun 2023 Yizhong Wang, Hamish Ivison, Pradeep Dasigi, Jack Hessel, Tushar Khot, Khyathi Raghavi Chandu, David Wadden, Kelsey MacMillan, Noah A. Smith, Iz Beltagy, Hannaneh Hajishirzi

Our evaluations show that the best model in any given evaluation reaches on average 83% of ChatGPT performance, and 68% of GPT-4 performance, suggesting that further investment in building better base models and instruction-tuning data is required to close the gap.

Instruction Following

SciFact-Open: Towards open-domain scientific claim verification

1 code implementation25 Oct 2022 David Wadden, Kyle Lo, Bailey Kuehl, Arman Cohan, Iz Beltagy, Lucy Lu Wang, Hannaneh Hajishirzi

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 a realistic setting against large corpora of scientific literature.

Claim Verification Information Retrieval +1

Generating Scientific Claims for Zero-Shot Scientific Fact Checking

1 code implementation ACL 2022 Dustin Wright, David Wadden, Kyle Lo, Bailey Kuehl, Arman Cohan, Isabelle Augenstein, Lucy Lu Wang

To address this challenge, we propose scientific claim generation, the task of generating one or more atomic and verifiable claims from scientific sentences, and demonstrate its usefulness in zero-shot fact checking for biomedical claims.

Fact Checking

MultiVerS: Improving scientific claim verification with weak supervision and full-document context

2 code implementations Findings (NAACL) 2022 David Wadden, Kyle Lo, Lucy Lu Wang, Arman Cohan, Iz Beltagy, Hannaneh Hajishirzi

Our approach outperforms two competitive baselines on three scientific claim verification datasets, with particularly strong performance in zero / few-shot domain adaptation experiments.

Claim Verification Domain Adaptation +1

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

no code implementations NAACL (sdp) 2021 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.

Claim Verification

Extracting a Knowledge Base of Mechanisms from COVID-19 Papers

3 code implementations NAACL 2021 Tom Hope, Aida Amini, David Wadden, Madeleine van Zuylen, Sravanthi Parasa, Eric Horvitz, Daniel Weld, Roy Schwartz, Hannaneh Hajishirzi

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.


The Effect of Moderation on Online Mental Health Conversations

no code implementations19 May 2020 David Wadden, Tal August, Qisheng Li, Tim Althoff

We found that participation in group mental health discussions led to improvements in psychological perspective, and that these improvements were larger in moderated conversations.

Fact or Fiction: Verifying Scientific Claims

2 code implementations EMNLP 2020 David Wadden, Shanchuan Lin, Kyle Lo, Lucy Lu Wang, Madeleine van Zuylen, Arman Cohan, Hannaneh Hajishirzi

We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that SUPPORTS or REFUTES a given scientific claim, and to identify rationales justifying each decision.

Claim Verification Domain Adaptation +1

Entity, Relation, and Event Extraction with Contextualized Span Representations

3 code implementations IJCNLP 2019 David Wadden, Ulme Wennberg, Yi Luan, Hannaneh Hajishirzi

We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction.

Event Extraction Joint Entity and Relation Extraction +3

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