276 citations · 2k across the 138 of their papers we have counts for
7 papers · 1 filter
VELMA: Verbalization Embodiment of LLM Agents for Vision and Language Navigation in Street View
Raphael Schumann, Wanrong Zhu, Weixi Feng +3
Incremental decision making in real-world environments is one of the most challenging tasks in embodied artificial intelligence. One particularly demanding scenario is Vision and L…
Mitigating Covertly Unsafe Text within Natural Language Systems
Alex Mei, Anisha Kabir, Sharon Levy +7
An increasingly prevalent problem for intelligent technologies is text safety, as uncontrolled systems may generate recommendations to their users that lead to injury or life-threa…
Environment-agnostic Multitask Learning for Natural Language Grounded Navigation
Xin Eric Wang, Vihan Jain, Eugene Ie +3
Recent research efforts enable study for natural language grounded navigation in photo-realistic environments, e.g., following natural language instructions or dialog. However, exi…
SafeRoute: Learning to Navigate Streets Safely in an Urban Environment
Sharon Levy, Wenhan Xiong, Elizabeth Belding +1
Recent studies show that 85% of women have changed their traveled route to avoid harassment and assault. Despite this, current mapping tools do not empower users with information t…
Variational Knowledge Graph Reasoning
Wenhu Chen, Wenhan Xiong, Xifeng Yan +1
Inferring missing links in knowledge graphs (KG) has attracted a lot of attention from the research community. In this paper, we tackle a practical query answering task involving p…
Efficient Inference and Learning in a Large Knowledge Base: Reasoning with Extracted Information using a Locally Groundable First-Order Probabilistic Logic
William Yang Wang, Kathryn Mazaitis, Ni Lao +2
One important challenge for probabilistic logics is reasoning with very large knowledge bases (KBs) of imperfect information, such as those produced by modern web-scale information…
Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic
William Yang Wang, Kathryn Mazaitis, William W. Cohen
In many probabilistic first-order representation systems, inference is performed by "grounding"---i.e., mapping it to a propositional representation, and then performing propositio…