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Found 9 papers, 2 papers with code
Attack Atlas: A Practitioner's Perspective on Challenges and Pitfalls in Red Teaming GenAI
As generative AI, particularly large language models (LLMs), become increasingly integrated into production applications, new attack surfaces and vulnerabilities emerge and put a focus on adversarial threats in natural language and multi-modal systems.
Programming Refusal with Conditional Activation Steering
In this paper, we propose Conditional Activation Steering (CAST), which analyzes LLM activation patterns during inference to selectively apply or withhold activation steering based on the input context.
CELL your Model: Contrastive Explanation Methods for Large Language Models
However, in the case of generative AI such as large language models (LLMs), there is no class prediction to explain.
Language Models in Dialogue: Conversational Maxims for Human-AI Interactions
Modern language models, while sophisticated, exhibit some inherent shortcomings, particularly in conversational settings.
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.
Reinforcement Learning in Non-Stationary Discrete-Time Linear-Quadratic Mean-Field Games
We propose an actor-critic algorithm to iteratively compute the mean-field equilibrium (MFE) of the LQ-MFG.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Information State Embedding in Partially Observable Cooperative Multi-Agent Reinforcement Learning
To enable the development of tractable algorithms, we introduce the concept of an information state embedding that serves to compress agents' histories.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Non-Cooperative Inverse Reinforcement Learning
Through interacting with the more informed player, the less informed player attempts to both infer, and act according to, the true objective function.
Online Planning for Decentralized Stochastic Control with Partial History Sharing
To demonstrate the applicability of the model, we propose a novel collaborative intrusion response model, where multiple agents (defenders) possessing asymmetric information aim to collaboratively defend a computer network.
