Latest Articles for Fine-tuning

This article explores neutrino mass through minor zeros in the mass matrix.

DP-ZO balances privacy and performance in language model training.

A new framework enhances model performance on unseen data using targeted changes.

New methods improve image editing speed and quality using smaller models.

New models explore stability of the weak scale in high energy physics.

Examining the effects of inter-dataset code duplication on model performance metrics.

A new method that improves pruned neural networks for better performance.

AI tools like SAM are reshaping how we map permafrost and understand climate change.

An overview of intrinsic and extrinsic criticality in biological systems.

This study enhances how language models handle math reasoning tasks.

Strategies to enhance QA models when labeled data is scarce.

AutoFT improves model performance on unseen data through innovative fine-tuning techniques.

A new method speeds up LLM text generation using additional prediction heads.

A new AI framework improves eye disease detection through enhanced imaging techniques.

A method improves efficiency in training and using large language models.

Research shows promise in using fine-tuned models for better rare disease understanding.

Study reveals risks of multilingual jailbreak attacks on large language models.

This article explores methods for using GPT-3.5 to automate code reviews effectively.

Analyzing the cost and efficiency of large language models in various tasks.

This study analyzes how language models handle familiar and unfamiliar topics.

Exploring the use of LLMs to analyze time series data across various fields.

DeRa offers a method to adjust language model alignment without retraining.

A method for fine-tuning language models using fewer parameters.

This article examines the impact of noise on language model performance.

Learn how new techniques improve the efficiency of large machine learning models.

New methods enhance the clarity of machine learning predictions.

New method enhances reliability of model predictions in real-world applications.

A new approach to integrate diverse cultural insights into language models.

A new method to spark creativity in problem-solving through AI support.

Exploring issues in creating decision-making AI models and solutions.

AV-SUPERB evaluates audio and visual models across various tasks for better performance.

New tools improve how systems retrieve information from long documents.

Exploring how language models reflect personality traits in recruitment.

This study enhances fine-tuning efficiency in neural networks using transductive active learning.

Enhancing Whisper's speech recognition for Vietnamese and other low-resource languages.

A new method for adapting LLMs without extensive labeling.

This article discusses a method to improve LLMs using verbal feedback without overgeneralization.

LoRETTA improves fine-tuning efficiency for large language models with fewer parameters.

Exploring the threats posed by indiscriminate data poisoning in self-supervised learning.

Examining how fine-tuning increases the risk of revealing sensitive training data.