1 citations · 1 across the 11 of their papers we have counts for
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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…