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Found 5 papers, 2 papers with code
DebUnc: Improving Large Language Model Agent Communication With Uncertainty Metrics
Multi-agent debates have been introduced to improve the accuracy of Large Language Models (LLMs) by having multiple agents discuss solutions to a problem over several rounds of debate.
OCTO+: A Suite for Automatic Open-Vocabulary Object Placement in Mixed Reality
We also introduce a benchmark for automatically evaluating the placement of virtual objects in augmented reality, alleviating the need for costly user studies.
OCTOPUS: Open-vocabulary Content Tracking and Object Placement Using Semantic Understanding in Mixed Reality
One key challenge in augmented reality is the placement of virtual content in natural locations.
An Exploration of Data Efficiency in Intra-Dataset Task Transfer for Dialog Understanding
Transfer learning is an exciting area of Natural Language Processing that has the potential to both improve model performance and increase data efficiency.
FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models.
