Large Language Model
Causal language modeling can elicit search and reasoning capabilities on logic puzzles
Causal language modeling using the Transformer architecture has yielded remarkable capabilities in Large Language Models (LLMs) over the last few years.
DeeR-VLA: Dynamic Inference of Multimodal Large Language Models for Efficient Robot Execution Y ang Yue
MLLM based on each situation at hand.
An Efficient Recipe for Long Context Extension via Middle-Focused Positional Encoding
Transformer-based Large Language Models (LLMs) are typically pre-trained with a fixed context window size, e.g ., 4K tokens in Touvron et al. [2023a].
BAM! Just Like That: Simple and Efficient Parameter Upcycling for Mixture of Experts
The Mixture of Experts (MoE) framework has become a popular architecture for large language models due to its superior performance over dense models. However, training MoEs from scratch in a large-scale regime is prohibitively expensive.
Large Pre-trained time series models for cross-domain Time series analysis tasks
However, most state-of-art neural models are known to be data-hungry and require substantial training data.
Bileve: Securing Text Provenance in Large Language Models Against Spoofing with Bi-level Signature
Text watermarks for large language models (LLMs) have been commonly used to identify the origins of machine-generated content, which is promising for assessing liability when combating deepfake or harmful content.
WAGLE: Strategic Weight Attribution for Effective and Modular Unlearning in Large Language Models
Despite growing interest, much of the existing research has focused on varied unlearning method designs to boost effectiveness and efficiency. However, the inherent relationship between model weights and LLM unlearning has not been extensively examined.