Search Results for author:
Found 5 papers, 1 papers with code
Detectors for Safe and Reliable LLMs: Implementations, Uses, and Limitations
Large language models (LLMs) are susceptible to a variety of risks, from non-faithful output to biased and toxic generations.
A Neuro-Symbolic Approach to Multi-Agent RL for Interpretability and Probabilistic Decision Making
To enable decision-making under uncertainty and partial observability, we developed a novel probabilistic neuro-symbolic framework, Probabilistic Logical Neural Networks (PLNN), which combines the capabilities of logical reasoning with probabilistic graphical models.
Learning Symbolic Rules over Abstract Meaning Representations for Textual Reinforcement Learning
Text-based reinforcement learning agents have predominantly been neural network-based models with embeddings-based representation, learning uninterpretable policies that often do not generalize well to unseen games.
MISMATCH: Fine-grained Evaluation of Machine-generated Text with Mismatch Error Types
In this work, we propose a new evaluation scheme to model human judgments in 7 NLP tasks, based on the fine-grained mismatches between a pair of texts.
A Unified View of Localized Kernel Learning
Multiple Kernel Learning, or MKL, extends (kernelized) SVM by attempting to learn not only a classifier/regressor but also the best kernel for the training task, usually from a combination of existing kernel functions.
