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Found 13 papers, 5 papers with code
MANGO: A Benchmark for Evaluating Mapping and Navigation Abilities of Large Language Models
The task is question-answering: for each maze, a large language model reads the walkthrough and answers hundreds of mapping and navigation questions such as "How should you go to Attic from West of House?"
A Wolf in Sheep's Clothing: Generalized Nested Jailbreak Prompts can Fool Large Language Models Easily
Finally, we analyze the failure of LLMs defense from the perspective of prompt execution priority, and propose corresponding defense strategies.
Algebraic and Statistical Properties of the Ordinary Least Squares Interpolator
Deep learning research has uncovered the phenomenon of benign overfitting for over-parameterized statistical models, which has drawn significant theoretical interest in recent years.
Causal inference in network experiments: regression-based analysis and design-based properties
Although we focus on regression-based point estimators and standard errors, our theory holds under the design-based framework, which assumes that the randomness comes solely from the design of network experiments and allows for arbitrary misspecification of the regression models.
Regression-Oriented Knowledge Distillation for Lightweight Ship Orientation Angle Prediction with Optical Remote Sensing Images
Four tiny SOAP models are also created by replacing the convolutional blocks in Mobile-SOAP with four small-scale networks, respectively.
Kernel-based off-policy estimation without overlap: Instance optimality beyond semiparametric efficiency
When it is violated, the classical semi-parametric efficiency bound can easily become infinite, so that the instance-optimal risk depends on the function class used to model the regression function.
Same Root Different Leaves: Time Series and Cross-Sectional Methods in Panel Data
A central goal in social science is to evaluate the causal effect of a policy.
Multi-Source Causal Inference Using Control Variates
Across simulations and two case studies with real data, we show that this control variate can significantly reduce the variance of the ATE estimate.
Conservative Wasserstein Training for Pose Estimation
We propose to incorporate inter-class correlations in a Wasserstein training framework by pre-defining ($i. e.,$ using arc length of a circle) or adaptively learning the ground metric.
Rerandomization and Regression Adjustment
R. A. Fisher suggested blocking on discrete covariates in the design stage or conducting analysis of covariance (ANCOVA) in the analysis stage.
Statistics Theory Methodology Statistics Theory
YNU Deep at SemEval-2018 Task 12: A BiLSTM Model with Neural Attention for Argument Reasoning Comprehension
This paper describes the system submitted to SemEval-2018 Task 12 (The Argument Reasoning Comprehension Task).
YNU\_Deep at SemEval-2018 Task 11: An Ensemble of Attention-based BiLSTM Models for Machine Comprehension
We firstly use GloVe to learn the distributed representations automatically from the instance, question and answer triples.
YNUDLG at IJCNLP-2017 Task 5: A CNN-LSTM Model with Attention for Multi-choice Question Answering in Examinations
In this paper, we perform convolutional neural networks (CNN) to learn the joint representations of question-answer pairs first, then use the joint representations as the inputs of the long short-term memory (LSTM) with attention to learn the answer sequence of a question for labeling the matching quality of each answer.
