Large Language Model
MoGU: A Framework for Enhancing Safety of LLMs While Preserving Their Usability
Large Language Models (LLMs) are increasingly deployed in various applications. As their usage grows, concerns regarding their safety are rising, especially in maintaining harmless responses when faced with malicious instructions.
AMAGO-2: Breaking the Multi-Task Barrier in Meta-Reinforcement Learning with Transformers
LLMs' flexibility is largely due to an "in-context learning" effect that emerges when
ACTOR S
We also demonstrate its effectiveness in generating robotic tasks.
How does Architecture Influence the Base Capabilities
Unlike existing work focusing on the influence of scale on base capabilities, our work examines the influence of architecture on those. Specifically, our concern is: How does architecture influence the base capabilities of pre-trained language models?
C-E VAL: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models Y uzhen Huang 1 Y uzhuo Bai
Evaluation benchmarks are at the core role for AI development.
End-to-End Ontology Learning with Large Language Models
Ontologies are useful for automatic machine processing of domain knowledge as they represent it in a structured format.
Transcendence: Generative Models Can Outperform The Experts That Train Them
Therefore, when trained on data generated by humans, we may not expect the artificial model to outperform the humans on their original objectives.
Guiding Large Language Models via Directional Stimulus Prompting
T5) to generate an auxiliary directional stimulus prompt for each input instance.