Large Language Models
Framing LLMs as products of complex supply chains rather than monolithic entities facilitates the creation of nuanced approaches to …
Generalization bounds for deep learning models are typically vacuous, not computable or restricted to specific model classes. In this …
Diffusion-based language models have recently emerged as a promising alternative to autoregressive generation, yet their reliance on …
Context-aided forecasting (CAF) holds promise for integrating domain knowledge and forward-looking information, enabling AI systems to …
Recent progress in large language models (LLMs) has focused on producing responses that meet human expectations and align with shared …
Context-aided forecasting (CAF) holds promise for integrating domain knowledge and forward-looking information, enabling AI systems to …
Building reliable computer-use agents requires grounding: accurately connecting natural language instructions to the correct on-screen …
Language model activations entangle concepts that mediate their behavior, making it difficult to interpret these factors, which has …
We introduce a framework for optimizing domain-specific dataset construction in foundation model training. Specifically, we seek a …
Neural sentence embedding models for dense retrieval typically rely on binary relevance labels, treating query-document pairs as …