Zobrazeno 1 - 10
of 245
pro vyhledávání: '"HOPE, TOM"'
Autor:
Pu, Kevin, Feng, K. J. Kevin, Grossman, Tovi, Hope, Tom, Mishra, Bhavana Dalvi, Latzke, Matt, Bragg, Jonathan, Chang, Joseph Chee, Siangliulue, Pao
Research ideation involves broad exploring and deep refining ideas. Both require deep engagement with literature. Existing tools focus primarily on idea broad generation, yet offer little support for iterative specification, refinement, and evaluatio
Externí odkaz:
http://arxiv.org/abs/2410.04025
Autor:
Forer, Lior, Hope, Tom
We address the fundamental task of inferring cross-document coreference and hierarchy in scientific texts, which has important applications in knowledge graph construction, search, recommendation and discovery. LLMs can struggle when faced with many
Externí odkaz:
http://arxiv.org/abs/2409.15113
The scientific ideation process often involves blending salient aspects of existing papers to create new ideas. To see if large language models (LLMs) can assist this process, we contribute Scideator, a novel mixed-initiative tool for scientific idea
Externí odkaz:
http://arxiv.org/abs/2409.14634
Autor:
Wadden, David, Shi, Kejian, Morrison, Jacob, Naik, Aakanksha, Singh, Shruti, Barzilay, Nitzan, Lo, Kyle, Hope, Tom, Soldaini, Luca, Shen, Shannon Zejiang, Downey, Doug, Hajishirzi, Hannaneh, Cohan, Arman
We present SciRIFF (Scientific Resource for Instruction-Following and Finetuning), a dataset of 137K instruction-following demonstrations for 54 tasks covering five essential scientific literature understanding capabilities: information extraction, s
Externí odkaz:
http://arxiv.org/abs/2406.07835
Autor:
Kuznetsov, Ilia, Afzal, Osama Mohammed, Dercksen, Koen, Dycke, Nils, Goldberg, Alexander, Hope, Tom, Hovy, Dirk, Kummerfeld, Jonathan K., Lauscher, Anne, Leyton-Brown, Kevin, Lu, Sheng, Mausam, Mieskes, Margot, Névéol, Aurélie, Pruthi, Danish, Qu, Lizhen, Schwartz, Roy, Smith, Noah A., Solorio, Thamar, Wang, Jingyan, Zhu, Xiaodan, Rogers, Anna, Shah, Nihar B., Gurevych, Iryna
The number of scientific articles produced every year is growing rapidly. Providing quality control over them is crucial for scientists and, ultimately, for the public good. In modern science, this process is largely delegated to peer review -- a dis
Externí odkaz:
http://arxiv.org/abs/2405.06563
Autor:
Munnangi, Monica, Feldman, Sergey, Wallace, Byron C, Amir, Silvio, Hope, Tom, Naik, Aakanksha
Despite their general capabilities, LLMs still struggle on biomedical NER tasks, which are difficult due to the presence of specialized terminology and lack of training data. In this work we set out to improve LLM performance on biomedical NER in lim
Externí odkaz:
http://arxiv.org/abs/2404.00152
We study the ability of LLMs to generate feedback for scientific papers and develop MARG, a feedback generation approach using multiple LLM instances that engage in internal discussion. By distributing paper text across agents, MARG can consume the f
Externí odkaz:
http://arxiv.org/abs/2401.04259
Various NLP tasks require a complex hierarchical structure over nodes, where each node is a cluster of items. Examples include generating entailment graphs, hierarchical cross-document coreference resolution, annotating event and subevent relations,
Externí odkaz:
http://arxiv.org/abs/2311.11301
Extracting fine-grained experimental findings from literature can provide dramatic utility for scientific applications. Prior work has developed annotation schemas and datasets for limited aspects of this problem, failing to capture the real-world co
Externí odkaz:
http://arxiv.org/abs/2311.09736