25 citations · 50 across the 7 of their papers we have counts for
7 papers
Abstract Reward Processes: Leveraging State Abstraction for Consistent Off-Policy Evaluation
Shreyas Chaudhari, Ameet Deshpande, Bruno Castro da Silva +1
Evaluating policies using off-policy data is crucial for applying reinforcement learning to real-world problems such as healthcare and autonomous driving. Previous methods for off-…
RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs
Shreyas Chaudhari, Pranjal Aggarwal, Vishvak Murahari +5
State-of-the-art large language models (LLMs) have become indispensable tools for various tasks. However, training LLMs to serve as effective assistants for humans requires careful…
Distraction-free Embeddings for Robust VQA
Atharvan Dogra, Deeksha Varshney, Ashwin Kalyan +2
The generation of effective latent representations and their subsequent refinement to incorporate precise information is an essential prerequisite for Vision-Language Understanding…
InstructEval: Systematic Evaluation of Instruction Selection Methods
Anirudh Ajith, Chris Pan, Mengzhou Xia +2
In-context learning (ICL) performs tasks by prompting a large language model (LLM) using an instruction and a small set of annotated examples called demonstrations. Recent work has…
Anthropomorphization of AI: Opportunities and Risks
Ameet Deshpande, Tanmay Rajpurohit, Karthik Narasimhan +1
Anthropomorphization is the tendency to attribute human-like traits to non-human entities. It is prevalent in many social contexts -- children anthropomorphize toys, adults do so w…
Toxicity in ChatGPT: Analyzing Persona-assigned Language Models
Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit +2
Large language models (LLMs) have shown incredible capabilities and transcended the natural language processing (NLP) community, with adoption throughout many services like healthc…