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Found 16 papers, 6 papers with code
Large Language Models Fall Short: Understanding Complex Relationships in Detective Narratives
Existing datasets for narrative understanding often fail to represent the complexity and uncertainty of relationships in real-life social scenarios.
The Mystery of In-Context Learning: A Comprehensive Survey on Interpretation and Analysis
Understanding in-context learning (ICL) capability that enables large language models (LLMs) to excel in proficiency through demonstration examples is of utmost importance.
NarrativePlay: Interactive Narrative Understanding
In this paper, we introduce NarrativePlay, a novel system that allows users to role-play a fictional character and interact with other characters in narratives such as novels in an immersive environment.
CUE: An Uncertainty Interpretation Framework for Text Classifiers Built on Pre-Trained Language Models
We then generate text representations by perturbing the latent space which causes fluctuation in predictive uncertainty.
propnet: Propagating 2D Annotation to 3D Segmentation for Gastric Tumors on CT Scans
This model consists of a proposing stage for coarse segmentation and a refining stage for improved segmentation, using two-way branches for enhanced performance and an up-down strategy for efficiency.
OverPrompt: Enhancing ChatGPT through Efficient In-Context Learning
The remarkable performance of pre-trained large language models has revolutionised various natural language processing applications.
Distilling ChatGPT for Explainable Automated Student Answer Assessment
Providing explainable and faithful feedback is crucial for automated student answer assessment.
NapSS: Paragraph-level Medical Text Simplification via Narrative Prompting and Sentence-matching Summarization
In this work, we propose a summarize-then-simplify two-stage strategy, which we call NapSS, identifying the relevant content to simplify while ensuring that the original narrative flow is preserved.
Data-Driven Distributionally Robust Scheduling of Community Integrated Energy Systems with Uncertain Renewable Generations Considering Integrated Demand Response
A community integrated energy system (CIES) is an important carrier of the energy internet and smart city in geographical and functional terms.
PHEE: A Dataset for Pharmacovigilance Event Extraction from Text
The primary goal of drug safety researchers and regulators is to promptly identify adverse drug reactions.
NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting
Financial forecasting has been an important and active area of machine learning research because of the challenges it presents and the potential rewards that even minor improvements in prediction accuracy or forecasting may entail.
Hierarchical Stochastic Scheduling of Multi-Community Integrated Energy Systems in Uncertain Environments via Stackelberg Game
An operating entity utilizing community-integrated energy systems with a large number of small-scale distributed energy sources can easily trade with existing distribution markets.
Optimal Scheduling of Integrated Demand Response-Enabled Community Integrated Energy Systems in Uncertain Environments
The community integrated energy system (CIES) is an essential energy internet carrier that has recently been the focus of much attention.
Privacy-preserving Spatiotemporal Scenario Generation of Renewable Energies: A Federated Deep Generative Learning Approach
Scenario generation is a fundamental and crucial tool for decision-making in power systems with high-penetration renewables.
Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis
While state-of-the-art NLP models have been achieving the excellent performance of a wide range of tasks in recent years, important questions are being raised about their robustness and their underlying sensitivity to systematic biases that may exist in their training and test data.
Deep Learning for Short-Term Voltage Stability Assessment of Power Systems
Secondly, a long short-term memory (LSTM) based assessment model is built through learning the time dependencies from the post-disturbance system dynamics.
