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Found 10 papers, 3 papers with code
Cross-Modality Safety Alignment
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.
medIKAL: Integrating Knowledge Graphs as Assistants of LLMs for Enhanced Clinical Diagnosis on EMRs
Electronic Medical Records (EMRs), while integral to modern healthcare, present challenges for clinical reasoning and diagnosis due to their complexity and information redundancy.
You Only Forward Once: Prediction and Rationalization in A Single Forward Pass
In our framework, A pre-trained language model like BERT is deployed to simultaneously perform prediction and rationalization with less impact from interlocking or spurious correlations.
MHLAT: Multi-hop Label-wise Attention Model for Automatic ICD Coding
International Classification of Diseases (ICD) coding is the task of assigning ICD diagnosis codes to clinical notes.
Faster Stochastic Variance Reduction Methods for Compositional MiniMax Optimization
All the proposed complexities indicate that our proposed methods can match lower bounds to existing minimax optimizations, without requiring a large batch size in each iteration.
Event Representation Learning Enhanced with External Commonsense Knowledge
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.
Learning Target-Specific Representations of Financial News Documents For Cumulative Abnormal Return Prediction
The model uses a target-sensitive representation of the news abstract to weigh sentences in the news content, so as to select and combine the most informative sentences for market modeling.
Learning Sentence Representations over Tree Structures for Target-Dependent Classification
To address above issues, we propose a reinforcement learning based approach, which automatically induces target-specific sentence representations over tree structures.
Knowledge-Driven Event Embedding for Stock Prediction
Representing structured events as vectors in continuous space offers a new way for defining dense features for natural language processing (NLP) applications.
