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Found 7 papers, 5 papers with code
Efficient In-Context Learning in Vision-Language Models for Egocentric Videos
Recent advancements in text-only large language models (LLMs) have highlighted the benefit of in-context learning for adapting to new tasks with a few demonstrations.
Constructing Temporal Dynamic Knowledge Graphs from Interactive Text-based Games
In order to address DGU's weaknesses while preserving its high interpretability, we propose the Temporal Discrete Graph Updater (TDGU), a novel neural network model that represents dynamic knowledge graphs as a sequence of timestamped graph events and models them using a temporal point based graph neural network.
Can Foundation Models Watch, Talk and Guide You Step by Step to Make a Cake?
Despite tremendous advances in AI, it remains a significant challenge to develop interactive task guidance systems that can offer situated, personalized guidance and assist humans in various tasks.
NLP Reproducibility For All: Understanding Experiences of Beginners
As natural language processing (NLP) has recently seen an unprecedented level of excitement, and more people are eager to enter the field, it is unclear whether current research reproducibility efforts are sufficient for this group of beginners to apply the latest developments.
DANLI: Deliberative Agent for Following Natural Language Instructions
These reactive agents are insufficient for long-horizon complex tasks.
Reproducibility Beyond the Research Community: Experience from NLP Beginners
As NLP research attracts public attention and excitement, it becomes increasingly important for it to be accessible to a broad audience.
One Model to Recognize Them All: Marginal Distillation from NER Models with Different Tag Sets
Named entity recognition (NER) is a fundamental component in the modern language understanding pipeline.
