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Found 6 papers, 2 papers with code
Evolving Knowledge Distillation with Large Language Models and Active Learning
In this paper, we propose EvoKD: Evolving Knowledge Distillation, which leverages the concept of active learning to interactively enhance the process of data generation using large language models, simultaneously improving the task capabilities of small domain model (student model).
Goal-Oriented Prompt Attack and Safety Evaluation for LLMs
There are several black-box attack methods, such as Prompt Attack, which can change the behaviour of LLMs and induce LLMs to generate unexpected answers with harmful contents.
RexUIE: A Recursive Method with Explicit Schema Instructor for Universal Information Extraction
In addition, we propose RexUIE, which is a Recursive Method with Explicit Schema Instructor for UIE.
Investigating the Robustness of Natural Language Generation from Logical Forms via Counterfactual Samples
To verify this hypothesis, we manually construct a set of counterfactual samples, which modify the original logical forms to generate counterfactual logical forms with rarely co-occurred table headers and logical operators.
ALL-IN-ONE: Multi-Task Learning BERT models for Evaluating Peer Assessments
Peer assessment has been widely applied across diverse academic fields over the last few decades and has demonstrated its effectiveness.
Convolutional Recurrent Neural Networks for Glucose Prediction
In addition, the model provides competitive performance in providing effective prediction horizon ($PH_{eff}$) with minimal time lag both in a simulated patient dataset ($PH_{eff}$ = 29. 0$\pm$0. 7 for 30-min and $PH_{eff}$ = 49. 8$\pm$2. 9 for 60-min) and in a real patient dataset ($PH_{eff}$ = 19. 3$\pm$3. 1 for 30-min and $PH_{eff}$ = 29. 3$\pm$9. 4 for 60-min).
