1 citations · 1 across the 11 of their papers we have counts for
11 papers
Robust Critics: Defending LLMs Against Multi-Turn Attacks
Roman Belaire, Arunesh Sinha, Pradeep Varakantham
When a user asks a language model something harmful, is it a genuine attack or a misunderstood but well-meaning question? This ambiguity is one of the central challenges of LLM saf…
Hey Chat, Can You Teach Me? Structuring Socratic Dialogue for Human Learning in the Wild
Sidney Tio, Arunesh Sinha, Pradeep Varakantham
Large language models are now widely used for everyday learning, but the underlying interactions are typically unstructured chats rather than following a curriculum. Unlike formal…
Induced Numerical Instability: Hidden Costs in Multimodal Large Language Models
Wai Tuck Wong, Jun Sun, Arunesh Sinha
The use of multimodal large language models has become widespread, and as such the study of these models and their failure points has become of utmost importance. We study a novel…
Strategic Incentivization for Locally Differentially Private Federated Learning
Yashwant Krishna Pagoti, Arunesh Sinha, Shamik Sural
In Federated Learning (FL), multiple clients jointly train a machine learning model by sharing gradient information, instead of raw data, with a server over multiple rounds. To add…
There are Levels to It: Red Teaming Dialogue With Hierarchical Reinforcement Learning
Roman Belaire, Arunesh Sinha, Pradeep Varakantham
Red teaming is essential for securing Large Language Models, yet current automated methods remain limited by templates and single-turn attacks. To simulate the complex, interactive…
On Learning Informative Trajectory Embeddings for Imitation, Classification and Regression
Zichang Ge, Changyu Chen, Arunesh Sinha +1
In real-world sequential decision making tasks like autonomous driving, robotics, and healthcare, learning from observed state-action trajectories is critical for tasks like imitat…
Semantic Loss Guided Data Efficient Supervised Fine Tuning for Safe Responses in LLMs
Yuxiao Lu, Arunesh Sinha, Pradeep Varakantham
Large Language Models (LLMs) generating unsafe responses to toxic prompts is a significant issue in their applications. While various efforts aim to address this safety concern, pr…
Heterogeneous Graph Generation: A Hierarchical Approach using Node Feature Pooling
Hritaban Ghosh, Chen Changyu, Arunesh Sinha +1
Heterogeneous graphs are present in various domains, such as social networks, recommendation systems, and biological networks. Unlike homogeneous graphs, heterogeneous graphs consi…
Towards Neural Network based Cognitive Models of Dynamic Decision-Making by Humans
Changyu Chen, Shashank Reddy Chirra, Maria José Ferreira +3
Modeling human cognitive processes in dynamic decision-making tasks has been an endeavor in AI for a long time because such models can help make AI systems more intuitive, personal…
Bootstrapping Language Models with DPO Implicit Rewards
Changyu Chen, Zichen Liu, Chao Du +5
Human alignment in large language models (LLMs) is an active area of research. A recent groundbreaking work, direct preference optimization (DPO), has greatly simplified the proces…
On Minimizing Adversarial Counterfactual Error in Adversarial RL
Roman Belaire, Arunesh Sinha, Pradeep Varakantham
Deep Reinforcement Learning (DRL) policies are highly susceptible to adversarial noise in observations, which poses significant risks in safety-critical scenarios. The challenge in…