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Aishwarya Padmakumar

9 accepted papers

2025

AEGIS2.0: A Diverse AI Safety Dataset and Risks Taxonomy for Alignment of LLM Guardrails

NAACL 2025long

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-quality, human-annotated datasets that address the full spectrum of LLM-related safety risks and are usable for commercial…

2024

VLN-Video: Utilizing Driving Videos for Outdoor Vision-and-Language Navigation

AAAI 2024technical

Outdoor Vision-and-Language Navigation (VLN) requires an agent to navigate through realistic 3D outdoor environments based on natural language instructions. The performance of existing VLN methods is limited by insufficient diversity in navigation environments and limited training data. To address t…

Cited by 8SourcePDFScholar
2023

KILM: Knowledge Injection into Encoder-Decoder Language Models

ACL 2023long

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 into Language Models (KILM), a novel approach that injects entity-related knowledge into encoder-decoder PLMs, via a gener…

2023

Multimodal Embodied Plan Prediction Augmented with Synthetic Embodied Dialogue

EMNLP 2023long main

Embodied task completion is a challenge where an agent in a simulated environment must predict environment actions to complete tasks based on natural language instructions and ego-centric visual observations. We propose a variant of this problem where the agent predicts actions at a higher level of…

Cited by 0SourceScholar
2022

ALFRED-L: Investigating the Role of Language for Action Learning in Interactive Visual Environments

EMNLP 2022main

Embodied Vision and Language Task Completion requires an embodied agent to interpret natural language instructions and egocentric visual observations to navigate through and interact with environments. In this work, we examine ALFRED, a challenging benchmark for embodied task completion, with the go…

2022

TEACh: Task-Driven Embodied Agents That Chat

AAAI 2022technical

Robots operating in human spaces must be able to engage in natural language interaction, both understanding and executing instructions, and using conversation to resolve ambiguity and correct mistakes. To study this, we introduce TEACh, a dataset of over 3,000 human-human, interactive dialogues to c…

2021

Dialog Policy Learning for Joint Clarification and Active Learning Queries

AAAI 2021technical

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 use of clarifications for correction and resolving uncertainty, and active learning queries to learn new concepts encount…

Cited by 10SourcePDFScholar
2019

Improving Grounded Natural Language Understanding through Human-Robot Dialog

ICRA 2019poster

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 environment to satisfy human commands, we can specify the language humans use to issue such commands, and connect concept word…

Cited by 85SourcecodeScholar
2017

Opportunistic Active Learning for Grounding Natural Language Descriptions

CoRL 2017

Active learning identifies data points from a pool of unlabeled examples whose labels, if made available, are most likely to improve the predictions of a supervised model. Most research on active learning assumes that an agent has access to the entire pool of unlabeled data and can ask for labels of

Cited by 0SourcePDFScholar