35 citations · 50 across the 18 of their papers we have counts for
19 papers
Training a General Purpose Automated Red Teaming Model
Aishwarya Padmakumar, Leon Derczynski, Traian Rebedea +1
Automated methods for red teaming LLMs are an important tool to identify LLM vulnerabilities that may not be covered in static benchmarks, allowing for more thorough probing. They…
Nemotron 3 Super: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Model for Agentic Reasoning
NVIDIA, :, Aakshita Chandiramani +544
We describe the pre-training, post-training, and quantization of Nemotron 3 Super, a 120 billion (active 12 billion) parameter hybrid Mamba-Attention Mixture-of-Experts model. Nemo…
Aegis2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment of LLM Guardrails
Shaona Ghosh, Prasoon Varshney, Makesh Narsimhan Sreedhar +4
As Large Language Models (LLMs) and generative AI become increasingly widespread, concerns about content safety have grown in parallel. Currently, there is a clear lack of high-qua…
QuAILoRA: Quantization-Aware Initialization for LoRA
Neal Lawton, Aishwarya Padmakumar, Judith Gaspers +4
QLoRA reduces the memory-cost of fine-tuning a large language model (LLM) with LoRA by quantizing the base LLM. However, quantization introduces quantization errors that negatively…
VLN-Video: Utilizing Driving Videos for Outdoor Vision-and-Language Navigation
Jialu Li, Aishwarya Padmakumar, Gaurav Sukhatme +1
Outdoor Vision-and-Language Navigation (VLN) requires an agent to navigate through realistic 3D outdoor environments based on natural language instructions. The performance of exis…
Data-Efficient Alignment of Large Language Models with Human Feedback Through Natural Language
Di Jin, Shikib Mehri, Devamanyu Hazarika +4
Learning from human feedback is a prominent technique to align the output of large language models (LLMs) with human expectations. Reinforcement learning from human feedback (RLHF)…
Alexa, play with robot: Introducing the First Alexa Prize SimBot Challenge on Embodied AI
Hangjie Shi, Leslie Ball, Govind Thattai +39
The Alexa Prize program has empowered numerous university students to explore, experiment, and showcase their talents in building conversational agents through challenges like the…
Multimodal Contextualized Plan Prediction for Embodied Task Completion
Mert İnan, Aishwarya Padmakumar, Spandana Gella +2
Task planning is an important component of traditional robotics systems enabling robots to compose fine grained skills to perform more complex tasks. Recent work building systems f…
KILM: Knowledge Injection into Encoder-Decoder Language Models
Yan Xu, Mahdi Namazifar, Devamanyu Hazarika +3
Large pre-trained language models (PLMs) have been shown to retain implicit knowledge within their parameters. To enhance this implicit knowledge, we propose Knowledge Injection in…
Dialog Acts for Task-Driven Embodied Agents
Spandana Gella, Aishwarya Padmakumar, Patrick Lange +1
Embodied agents need to be able to interact in natural language understanding task descriptions and asking appropriate follow up questions to obtain necessary information to be eff…
On the Limits of Evaluating Embodied Agent Model Generalization Using Validation Sets
Hyounghun Kim, Aishwarya Padmakumar, Di Jin +2
Natural language guided embodied task completion is a challenging problem since it requires understanding natural language instructions, aligning them with egocentric visual observ…
Rome was built in 1776: A Case Study on Factual Correctness in Knowledge-Grounded Response Generation
Sashank Santhanam, Behnam Hedayatnia, Spandana Gella +4
Recently neural response generation models have leveraged large pre-trained transformer models and knowledge snippets to generate relevant and informative responses. However, this…
TEACh: Task-driven Embodied Agents that Chat
Aishwarya Padmakumar, Jesse Thomason, Ayush Shrivastava +6
Robots operating in human spaces must be able to engage in natural language interaction with people, both understanding and executing instructions, and using conversation to resolv…
Generative Conversational Networks
Alexandros Papangelis, Karthik Gopalakrishnan, Aishwarya Padmakumar +3
Inspired by recent work in meta-learning and generative teaching networks, we propose a framework called Generative Conversational Networks, in which conversational agents learn to…
Dialog as a Vehicle for Lifelong Learning
Aishwarya Padmakumar, Raymond J. Mooney
Dialog systems research has primarily been focused around two main types of applications - task-oriented dialog systems that learn to use clarification to aid in understanding a go…
Dialog Policy Learning for Joint Clarification and Active Learning Queries
Aishwarya Padmakumar, Raymond J. Mooney
Intelligent systems need to be able to recover from mistakes, resolve uncertainty, and adapt to novel concepts not seen during training. Dialog interaction can enable this by the u…
Improving Grounded Natural Language Understanding through Human-Robot Dialog
Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov +6
Natural language understanding for robotics can require substantial domain- and platform-specific engineering. For example, for mobile robots to pick-and-place objects in an enviro…
Interaction and Autonomy in RoboCup@Home and Building-Wide Intelligence
Justin Hart, Harel Yedidsion, Yuqian Jiang +8
Efforts are underway at UT Austin to build autonomous robot systems that address the challenges of long-term deployments in office environments and of the more prescribed domestic…
Learning a Policy for Opportunistic Active Learning
Aishwarya Padmakumar, Peter Stone, Raymond J. Mooney
Active learning identifies data points to label that are expected to be the most useful in improving a supervised model. Opportunistic active learning incorporates active learning…