Search Results for author:
Found 20 papers, 6 papers with code
FLAP: Flow-Adhering Planning with Constrained Decoding in LLMs
To study this, we propose the problem of faithful planning in TODs that needs to resolve user intents by following predefined flows and preserving API dependencies.
Can Your Model Tell a Negation from an Implicature? Unravelling Challenges With Intent Encoders
The triplet task gauges the model's understanding of two semantic concepts paramount in real-world conversational systems-- negation and implicature.
DeAL: Decoding-time Alignment for Large Language Models
Current work focuses on alignment at model training time, through techniques such as Reinforcement Learning with Human Feedback (RLHF).
Measuring and Mitigating Constraint Violations of In-Context Learning for Utterance-to-API Semantic Parsing
In executable task-oriented semantic parsing, the system aims to translate users' utterances in natural language to machine-interpretable programs (API calls) that can be executed according to pre-defined API specifications.
Parameter and Data Efficient Continual Pre-training for Robustness to Dialectal Variance in Arabic
While prior research studies have tried to adapt these multilingual models for dialectal variants of Arabic, it still remains a challenging problem owing to the lack of sufficient monolingual dialectal data and parallel translation data of such dialectal variants.
Robustification of Multilingual Language Models to Real-world Noise in Crosslingual Zero-shot Settings with Robust Contrastive Pretraining
To benchmark the performance of pretrained multilingual language models, we construct noisy datasets covering five languages and four NLP tasks and observe a clear gap in the performance between clean and noisy data in the zero-shot cross-lingual setting.
Data Augmentation
Pretrained Multilingual Language Models
+1
On the Robustness of Intent Classification and Slot Labeling in Goal-oriented Dialog Systems to Real-world Noise
In this work, we investigate how robust IC/SL models are to noisy data.
Learning Movement Strategies for Moving Target Defense
We argue that existing models are inadequate in sequential settings when there is incomplete information about rational adversary and yield sub-optimal movement strategies.
RADAR-X: An Interactive Mixed Initiative Planning Interface Pairing Contrastive Explanations and Revised Plan Suggestions
Decision support systems seek to enable informed decision-making.
Moving Target Defense for Robust Monitoring of Electric Grid Transformers in Adversarial Environments
In this paper, we draw inspiration from work in Moving Target Defense (MTD) and consider a dynamic monitoring strategy that makes it difficult for an attacker to prevent unique identification of behavioral signals that indicate the status of HVTs.
Computer Science and Game Theory
Multi-agent Reinforcement Learning in Bayesian Stackelberg Markov Games for Adaptive Moving Target Defense
We argue that existing models are inadequate in sequential settings when there is incomplete information about a rational adversary and yield sub-optimal movement strategies.
Not all Failure Modes are Created Equal: Training Deep Neural Networks for Explicable (Mis)Classification
classifying the image of a dog to an airplane) can perplex humans and result in the loss of human trust in the system.
`Why didn't you allocate this task to them?' Negotiation-Aware Explicable Task Allocation and Contrastive Explanation Generation
Task allocation is an important problem in multi-agent systems.
Imperfect ImaGANation: Implications of GANs Exacerbating Biases on Facial Data Augmentation and Snapchat Selfie Lenses
In this paper, we show that popular Generative Adversarial Networks (GANs) exacerbate biases along the axes of gender and skin tone when given a skewed distribution of face-shots.
Inference of Human's Observation Strategy for Monitoring Robot's Behavior based on a Game-Theoretic Model of Trust
Thus, we define the concept of a trust boundary over the mixed strategy space of the human and show that it helps to discover optimal monitoring strategies.
Markov Game Modeling of Moving Target Defense for Strategic Detection of Threats in Cloud Networks
The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions.
Imagining an Engineer: On GAN-Based Data Augmentation Perpetuating Biases
The use of synthetic data generated by Generative Adversarial Networks (GANs) has become quite a popular method to do data augmentation for many applications.
MTDeep: Boosting the Security of Deep Neural Nets Against Adversarial Attacks with Moving Target Defense
Present attack methods can make state-of-the-art classification systems based on deep neural networks misclassify every adversarially modified test example.
Compliant Conditions for Polynomial Time Approximation of Operator Counts
In this paper, we develop a computationally simpler version of the operator count heuristic for a particular class of domains.
Moving Target Defense for Web Applications using Bayesian Stackelberg Games
We also address the issue of prioritizing vulnerabilities that when fixed, improves the security of the MTD system.
