no code implementations • 21 Jan 2025 • Pha Nguyen, Sailik Sengupta, Girik Malik, Arshit Gupta, Bonan Min
The improved competence of generative models can help building multi-modal virtual assistants that leverage modalities beyond language.
no code implementations • 6 Dec 2024 • Subhojyoti Mukherjee, Anusha Lalitha, Sailik Sengupta, Aniket Deshmukh, Branislav Kveton
Multi-objective alignment from human feedback (MOAHF) in large language models (LLMs) is a challenging problem as human preferences are complex, multifaceted, and often conflicting.
no code implementations • 12 Oct 2024 • Jongwoo Ko, Saket Dingliwal, Bhavana Ganesh, Sailik Sengupta, Sravan Bodapati, Aram Galstyan
Direct alignment algorithms (DAAs), such as direct preference optimization (DPO), have become popular alternatives for Reinforcement Learning from Human Feedback (RLHF) due to their simplicity, efficiency, and stability.
no code implementations • 9 Mar 2024 • Shamik Roy, Sailik Sengupta, Daniele Bonadiman, Saab Mansour, Arshit Gupta
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.
no code implementations • 7 Mar 2024 • Yuwei Zhang, Siffi Singh, Sailik Sengupta, Igor Shalyminov, Hang Su, Hwanjun Song, Saab Mansour
The triplet task gauges the model's understanding of two semantic concepts paramount in real-world conversational systems-- negation and implicature.
no code implementations • 5 Feb 2024 • James Y. Huang, Sailik Sengupta, Daniele Bonadiman, Yi-An Lai, Arshit Gupta, Nikolaos Pappas, Saab Mansour, Katrin Kirchhoff, Dan Roth
Current work focuses on alignment at model training time, through techniques such as Reinforcement Learning with Human Feedback (RLHF).
no code implementations • 24 May 2023 • Shufan Wang, Sebastien Jean, Sailik Sengupta, James Gung, Nikolaos Pappas, Yi Zhang
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.
no code implementations • 8 Nov 2022 • Soumajyoti Sarkar, Kaixiang Lin, Sailik Sengupta, Leonard Lausen, Sheng Zha, Saab Mansour
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.
1 code implementation • 10 Oct 2022 • Asa Cooper Stickland, Sailik Sengupta, Jason Krone, Saab Mansour, He He
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
1 code implementation • EMNLP (NLP4ConvAI) 2021 • Sailik Sengupta, Jason Krone, Saab Mansour
In this work, we investigate how robust IC/SL models are to noisy data.
no code implementations • 1 Jan 2021 • Sailik Sengupta, Subbarao Kambhampati
We argue that existing models are inadequate in sequential settings when there is incomplete information about rational adversary and yield sub-optimal movement strategies.
no code implementations • 19 Nov 2020 • Karthik Valmeekam, Sarath Sreedharan, Sailik Sengupta, Subbarao Kambhampati
Decision support systems seek to enable informed decision-making.
1 code implementation • 8 Oct 2020 • Sailik Sengupta, Kaustav Basu, Arunabha Sen, Subbarao Kambhampati
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
no code implementations • 20 Jul 2020 • Sailik Sengupta, Subbarao Kambhampati
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.
no code implementations • 26 Jun 2020 • Alberto Olmo, Sailik Sengupta, Subbarao Kambhampati
classifying the image of a dog to an airplane) can perplex humans and result in the loss of human trust in the system.
no code implementations • 5 Feb 2020 • Zahra Zahedi, Sailik Sengupta, Subbarao Kambhampati
Task allocation is an important problem in multi-agent systems.
no code implementations • 26 Jan 2020 • Niharika Jain, Alberto Olmo, Sailik Sengupta, Lydia Manikonda, Subbarao Kambhampati
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.
no code implementations • 1 Mar 2019 • Zahra Zahedi, Sailik Sengupta, Subbarao Kambhampati
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.
2 code implementations • 23 Dec 2018 • Ankur Chowdhary, Sailik Sengupta, Dijiang Huang, Subbarao Kambhampati
The processing and storage of critical data in large-scale cloud networks necessitate the need for scalable security solutions.
no code implementations • 9 Nov 2018 • Niharika Jain, Lydia Manikonda, Alberto Olmo Hernandez, Sailik Sengupta, Subbarao Kambhampati
The use of synthetic data generated by Generative Adversarial Networks (GANs) has become quite a popular method to do data augmentation for many applications.
1 code implementation • 19 May 2017 • Sailik Sengupta, Tathagata Chakraborti, Subbarao Kambhampati
Present attack methods can make state-of-the-art classification systems based on deep neural networks misclassify every adversarially modified test example.
no code implementations • 25 May 2016 • Tathagata Chakraborti, Sarath Sreedharan, Sailik Sengupta, T. K. Satish Kumar, Subbarao Kambhampati
In this paper, we develop a computationally simpler version of the operator count heuristic for a particular class of domains.
1 code implementation • 23 Feb 2016 • Sailik Sengupta, Satya Gautam Vadlamudi, Subbarao Kambhampati, Marthony Taguinod, Adam Doupé, Ziming Zhao, Gail-Joon Ahn
We also address the issue of prioritizing vulnerabilities that when fixed, improves the security of the MTD system.