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pro vyhledávání: '"Duan, Junwen"'
Unsupervised rationale extraction aims to extract text snippets to support model predictions without explicit rationale annotation. Researchers have made many efforts to solve this task. Previous works often encode each aspect independently, which ma
Externí odkaz:
http://arxiv.org/abs/2410.03531
Unified information extraction (UIE) aims to complete all information extraction tasks using a single model or framework. While previous work has primarily focused on instruction-tuning large language models (LLMs) with constructed datasets, these me
Externí odkaz:
http://arxiv.org/abs/2409.11673
Autor:
Wang, Siyin, Ye, Xingsong, Cheng, Qinyuan, Duan, Junwen, Li, Shimin, Fu, Jinlan, Qiu, Xipeng, Huang, Xuanjing
As Artificial General Intelligence (AGI) becomes increasingly integrated into various facets of human life, ensuring the safety and ethical alignment of such systems is paramount. Previous studies primarily focus on single-modality threats, which may
Externí odkaz:
http://arxiv.org/abs/2406.15279
Electronic Medical Records (EMRs), while integral to modern healthcare, present challenges for clinical reasoning and diagnosis due to their complexity and information redundancy. To address this, we proposed medIKAL (Integrating Knowledge Graphs as
Externí odkaz:
http://arxiv.org/abs/2406.14326
Unsupervised rationale extraction aims to extract concise and contiguous text snippets to support model predictions without any annotated rationale. Previous studies have used a two-phase framework known as the Rationalizing Neural Prediction (RNP) f
Externí odkaz:
http://arxiv.org/abs/2311.02344
International Classification of Diseases (ICD) coding is the task of assigning ICD diagnosis codes to clinical notes. This can be challenging given the large quantity of labels (nearly 9,000) and lengthy texts (up to 8,000 tokens). However, unlike th
Externí odkaz:
http://arxiv.org/abs/2309.08868
This paper delves into the realm of stochastic optimization for compositional minimax optimization - a pivotal challenge across various machine learning domains, including deep AUC and reinforcement learning policy evaluation. Despite its significanc
Externí odkaz:
http://arxiv.org/abs/2308.09604
Prior work has proposed effective methods to learn event representations that can capture syntactic and semantic information over text corpus, demonstrating their effectiveness for downstream tasks such as script event prediction. On the other hand,
Externí odkaz:
http://arxiv.org/abs/1909.05190
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