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Found 8 papers, 6 papers with code
LESS: Selecting Influential Data for Targeted Instruction Tuning
Instruction tuning has unlocked powerful capabilities in large language models (LLMs), effectively using combined datasets to develop generalpurpose chatbots.
The Marginal Value of Momentum for Small Learning Rate SGD
Momentum is known to accelerate the convergence of gradient descent in strongly convex settings without stochastic gradient noise.
Trainable Transformer in Transformer
In this work, we propose an efficient construction, Transformer in Transformer (in short, TinT), that allows a transformer to simulate and fine-tune complex models internally during inference (e. g., pre-trained language models).
Fine-Tuning Language Models with Just Forward Passes
Fine-tuning language models (LMs) has yielded success on diverse downstream tasks, but as LMs grow in size, backpropagation requires a prohibitively large amount of memory.
A Kernel-Based View of Language Model Fine-Tuning
It has become standard to solve NLP tasks by fine-tuning pre-trained language models (LMs), especially in low-data settings.
On the SDEs and Scaling Rules for Adaptive Gradient Algorithms
Approximating Stochastic Gradient Descent (SGD) as a Stochastic Differential Equation (SDE) has allowed researchers to enjoy the benefits of studying a continuous optimization trajectory while carefully preserving the stochasticity of SGD.
On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs)
It is generally recognized that finite learning rate (LR), in contrast to infinitesimal LR, is important for good generalization in real-life deep nets.
A Mathematical Exploration of Why Language Models Help Solve Downstream Tasks
This paper initiates a mathematical study of this phenomenon for the downstream task of text classification by considering the following questions: (1) What is the intuitive connection between the pretraining task of next word prediction and text classification?
