Large Language Model
Reranking Laws for Language Generation: A Communication-Theoretic Perspective
To ensure large language models (LLMs) are used safely, one must reduce their propensity to hallucinate or to generate unacceptable answers. A simple and often used strategy is to first let the LLM generate multiple hypotheses and then employ a reranker to choose the best one.
GAVEL: Generating Games Via Evolution and Language Models Graham T odd New York University Tandon Brooklyn, New York, USA gdrtodd Alexander G. Padula ETH Zurich Zurich, Switzerland apadula
Automatically generating novel and interesting games is a complex task.
RestoreAgent: Autonomous Image Restoration Agent via Multimodal Large Language Models
Natural images captured by mobile devices often suffer from multiple types of degradation, such as noise, blur, and low light.
BiasandVolatility: AStatisticalFrameworkfor EvaluatingLargeLanguageModel'sStereotypesand theAssociatedGenerationInconsistency
The biases ofSnow are manifested in the statistical properties of itsperspectives(i.e.