[PDF][PDF] Assessing the ineffectiveness of synthetic reinforcement learning feedback in fine-tuning large language models
S Whitmore, C Harrington, E Pritchard - 2024 - osf.io
The rapid evolution of artificial intelligence has brought significant advancements in various
applications, yet fine-tuning models to align outputs with user needs and ethical standards …
applications, yet fine-tuning models to align outputs with user needs and ethical standards …
Hardware accelerator design for sparse dnn inference and training: A tutorial
Deep neural networks (DNNs) are widely used in many fields, such as artificial intelligence
generated content (AIGC) and robotics. To efficiently support these tasks, the model pruning …
generated content (AIGC) and robotics. To efficiently support these tasks, the model pruning …
Q-galore: Quantized galore with int4 projection and layer-adaptive low-rank gradients
Training Large Language Models (LLMs) is memory-intensive due to the large number of
parameters and associated optimization states. GaLore, a recent method, reduces memory …
parameters and associated optimization states. GaLore, a recent method, reduces memory …
A Comprehensive Performance Study of Large Language Models on Novel AI Accelerators
Artificial intelligence (AI) methods have become critical in scientific applications to help
accelerate scientific discovery. Large language models (LLMs) are being considered as a …
accelerate scientific discovery. Large language models (LLMs) are being considered as a …
Efficient Deep Learning Infrastructures for Embedded Computing Systems: A Comprehensive Survey and Future Envision
Deep neural networks (DNNs) have recently achieved impressive success across a wide
range of real-world vision and language processing tasks, spanning from image …
range of real-world vision and language processing tasks, spanning from image …
Enhancing zero-shot crypto sentiment with fine-tuned language model and prompt engineering
Blockchain technology has revolutionized the financial landscape, witnessing widespread
adoption of cryptocurrencies due to their decentralized and transparent nature. As …
adoption of cryptocurrencies due to their decentralized and transparent nature. As …
Sparsity-Accelerated Training for Large Language Models
Large language models (LLMs) have demonstrated proficiency across various natural
language processing (NLP) tasks but often require additional training, such as continual pre …
language processing (NLP) tasks but often require additional training, such as continual pre …
SLoPe: Double-Pruned Sparse Plus Lazy Low-Rank Adapter Pretraining of LLMs
M Mozaffari, A Yazdanbakhsh, Z Zhang… - arXiv preprint arXiv …, 2024 - arxiv.org
We propose SLoPe, a Double-Pruned Sparse Plus Lazy Low-rank Adapter Pretraining
method for LLMs that improves the accuracy of sparse LLMs while accelerating their …
method for LLMs that improves the accuracy of sparse LLMs while accelerating their …
Deciphering Compatibility Relationships with Textual Descriptions via Extraction and Explanation
Understanding and accurately explaining compatibility relationships between fashion items
is a challenging problem in the burgeoning domain of AI-driven outfit recommendations …
is a challenging problem in the burgeoning domain of AI-driven outfit recommendations …
ProMoAI: Process Modeling with Generative AI
ProMoAI is a novel tool that leverages Large Language Models (LLMs) to automatically
generate process models from textual descriptions, incorporating advanced prompt …
generate process models from textual descriptions, incorporating advanced prompt …