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Found 12 papers, 8 papers with code
TextGuard: Provable Defense against Backdoor Attacks on Text Classification
In this work, we propose TextGuard, the first provable defense against backdoor attacks on text classification.
DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models
Yet, while the literature on the trustworthiness of GPT models remains limited, practitioners have proposed employing capable GPT models for sensitive applications such as healthcare and finance -- where mistakes can be costly.
Better Context Makes Better Code Language Models: A Case Study on Function Call Argument Completion
To better solve this task, we query a program analyzer for information relevant to a given function call, and consider ways to provide the analyzer results to different code completion models during inference and training.
Zero-Shot Classification by Logical Reasoning on Natural Language Explanations
To this end, we propose the framework CLORE (Classification by LOgical Reasoning on Explanations).
Your Autoregressive Generative Model Can be Better If You Treat It as an Energy-Based One
Autoregressive generative models are commonly used, especially for those tasks involving sequential data.
Towards Generating Real-World Time Series Data
In this paper, we propose a novel generative framework for RTS data - RTSGAN to tackle the aforementioned challenges.
Improving Certified Robustness via Statistical Learning with Logical Reasoning
Intensive algorithmic efforts have been made to enable the rapid improvements of certificated robustness for complex ML models recently.
Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States
Our framework aims to address two unique challenges in financial PM: (1) data heterogeneity -- the collected information for each asset is usually diverse, noisy and imbalanced (e. g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
T3: Tree-Autoencoder Constrained Adversarial Text Generation for Targeted Attack
In particular, we propose a tree-based autoencoder to embed the discrete text data into a continuous representation space, upon which we optimize the adversarial perturbation.
The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks
This paper studies model-inversion attacks, in which the access to a model is abused to infer information about the training data.
AdvCodec: Towards A Unified Framework for Adversarial Text Generation
In particular, we propose a tree based autoencoder to encode discrete text data into continuous vector space, upon which we optimize the adversarial perturbation.
A Concise Model for Multi-Criteria Chinese Word Segmentation with Transformer Encoder
Multi-criteria Chinese word segmentation (MCCWS) aims to exploit the relations among the multiple heterogeneous segmentation criteria and further improve the performance of each single criterion.
