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Found 6 papers, 1 papers with code
Energy-Based Preference Model Offers Better Offline Alignment than the Bradley-Terry Preference Model
As a better alternative, we propose an energy-based model (EBM) that always has a unique MLE, inherently satisfying the linearity requirement.
Preference-Oriented Supervised Fine-Tuning: Favoring Target Model Over Aligned Large Language Models
Alignment, endowing a pre-trained Large language model (LLM) with the ability to follow instructions, is crucial for its real-world applications.
BoRA: Bi-dimensional Weight-Decomposed Low-Rank Adaptation
In recent years, Parameter-Efficient Fine-Tuning (PEFT) methods like Low-Rank Adaptation (LoRA) have significantly enhanced the adaptability of large-scale pre-trained models.
Interactive-T2S: Multi-Turn Interactions for Text-to-SQL with Large Language Models
This study explores text-to-SQL parsing by leveraging the powerful reasoning capabilities of large language models (LLMs).
A large calcium-imaging dataset reveals a systematic V4 organization for natural scenes
To gain deeper insights into visual processing of natural scenes, we utilized widefield calcium-imaging of primate V4 in response to many natural images, generating a large dataset of columnar-scale responses.
Uncertainty Sentence Sampling by Virtual Adversarial Perturbation
Active learning for sentence understanding attempts to reduce the annotation cost by identifying the most informative examples.
