Search Results for author:
Found 24 papers, 18 papers with code
Identifying and Mitigating Vulnerabilities in LLM-Integrated Applications
Successful exploits of the identified vulnerabilities result in the users receiving responses tailored to the intent of a threat initiator.
InstructRetro: Instruction Tuning post Retrieval-Augmented Pretraining
After instruction tuning on Retro, InstructRetro demonstrates significant improvement over the instruction tuned GPT on a wide range of zero-shot tasks.
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.
Can Public Large Language Models Help Private Cross-device Federated Learning?
We study (differentially) private federated learning (FL) of language models.
Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study
Thus, it is still an open question: shall we pretrain large autoregressive LMs with retrieval?
FOCUS: Fairness via Agent-Awareness for Federated Learning on Heterogeneous Data
However, due to the heterogeneous nature of local data, it is challenging to optimize or even define fairness of the trained global model for the agents.
SemAttack: Natural Textual Attacks via Different Semantic Spaces
In particular, SemAttack optimizes the generated perturbations constrained on generic semantic spaces, including typo space, knowledge space (e. g., WordNet), contextualized semantic space (e. g., the embedding space of BERT clusterings), or the combination of these spaces.
Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models
In this work, we systematically explore domain-adaptive training to reduce the toxicity of language models.
Certifying Out-of-Domain Generalization for Blackbox Functions
As a result, the wider application of these techniques is currently limited by its scalability and flexibility -- these techniques often do not scale to large-scale datasets with modern deep neural networks or cannot handle loss functions which may be non-smooth such as the 0-1 loss.
Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models
In this paper, we present Adversarial GLUE (AdvGLUE), a new multi-task benchmark to quantitatively and thoroughly explore and evaluate the vulnerabilities of modern large-scale language models under various types of adversarial attacks.
Ranked #1 on
Adversarial Robustness
on AdvGLUE
Counterfactual Adversarial Learning with Representation Interpolation
Deep learning models exhibit a preference for statistical fitting over logical reasoning.
Incorporating External POS Tagger for Punctuation Restoration
Punctuation restoration is an important post-processing step in automatic speech recognition.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation
In this paper, we aim to explore the power of generative models and gradient sparsity, and propose a scalable privacy-preserving generative model DATALENS.
InfoBERT: Improving Robustness of Language Models from An Information Theoretic Perspective
Large-scale language models such as BERT have achieved state-of-the-art performance across a wide range of NLP tasks.
Ranked #3 on
Natural Language Inference
on ANLI test
(using extra training data)
Uncovering the Connections Between Adversarial Transferability and Knowledge Transferability
Knowledge transferability, or transfer learning, has been widely adopted to allow a pre-trained model in the source domain to be effectively adapted to downstream tasks in the target domain.
Towards Evaluating the Robustness of Chinese BERT Classifiers
Recent advances in large-scale language representation models such as BERT have improved the state-of-the-art performances in many NLP tasks.
Revisit Systematic Generalization via Meaningful Learning
Humans can systematically generalize to novel compositions of existing concepts.
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.
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.
Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms
The most surprising result is that for unweighted $K$NN classifiers and regressors, the Shapley value of all $N$ data points can be computed, exactly, in $O(N\log N)$ time -- an exponential improvement on computational complexity!
G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators
In particular, we train a student data generator with an ensemble of teacher discriminators and propose a novel private gradient aggregation mechanism to ensure differential privacy on all information that flows from teacher discriminators to the student generator.
Towards Efficient Data Valuation Based on the Shapley Value
In this paper, we study the problem of data valuation by utilizing the Shapley value, a popular notion of value which originated in cooperative game theory.
