Search Results for author:
Found 6 papers, 4 papers with code
Learning vs Retrieval: The Role of In-Context Examples in Regression with LLMs
In this work, we propose a framework for evaluating in-context learning mechanisms, which we claim are a combination of retrieving internal knowledge and learning from in-context examples by focusing on regression tasks.
Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language
This approach empowers domain experts, even those not well-versed in ML/AI, to formally declare their knowledge to be incorporated in customized neural models in the DomiKnowS framework.
Probabilistic Reasoning in Generative Large Language Models
This paper considers the challenges Large Language Models (LLMs) face when reasoning over text that includes information involving uncertainty explicitly quantified via probability values.
Teaching Probabilistic Logical Reasoning to Transformers
In this paper, we evaluate the capability of transformer-based language models in making inferences over uncertain text that includes uncertain rules of reasoning.
GLUECons: A Generic Benchmark for Learning Under Constraints
Recent research has shown that integrating domain knowledge into deep learning architectures is effective -- it helps reduce the amount of required data, improves the accuracy of the models' decisions, and improves the interpretability of models.
DomiKnowS: A Library for Integration of Symbolic Domain Knowledge in Deep Learning
We demonstrate a library for the integration of domain knowledge in deep learning architectures.
