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Found 10 papers, 3 papers with code
KnowledgePrompts: Exploring the Abilities of Large Language Models to Solve Proportional Analogies via Knowledge-Enhanced Prompting
Making analogies is fundamental to cognition.
Knowledge Graphs of Driving Scenes to Empower the Emerging Capabilities of Neurosymbolic AI
In the era of Generative AI, Neurosymbolic AI is emerging as a powerful approach for tasks spanning from perception to cognition.
Towards Infusing Auxiliary Knowledge for Distracted Driver Detection
Distracted driving is a leading cause of road accidents globally.
Evaluating the Role of Data Enrichment Approaches Towards Rare Event Analysis in Manufacturing
This paper evaluates the role of data enrichment techniques combined with supervised machine-learning techniques for rare event detection and prediction.
On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models
Through our analysis, we find that LLMs' ability to identify sentence analogies is positively correlated with their ability to encode syntactic and semantic structures of sentences.
A Comprehensive Survey on Rare Event Prediction
This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, data processing, algorithmic approaches, and evaluation approaches.
ANALOGICAL -- A Novel Benchmark for Long Text Analogy Evaluation in Large Language Models
Over the past decade, analogies, in the form of word-level analogies, have played a significant role as an intrinsic measure of evaluating the quality of word embedding methods such as word2vec.
Knowledge-based Entity Prediction for Improved Machine Perception in Autonomous Systems
Knowledge-based entity prediction (KEP) is a novel task that aims to improve machine perception in autonomous systems.
Neuro-symbolic Architectures for Context Understanding
Computational context understanding refers to an agent's ability to fuse disparate sources of information for decision-making and is, therefore, generally regarded as a prerequisite for sophisticated machine reasoning capabilities, such as in artificial intelligence (AI).
An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice
With the expectation that neuro-symbolic fusion through KGEs will improve scene understanding, this research explores the generation and evaluation of KGEs for autonomous driving data.
