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Found 4 papers, 0 papers with code
CURATRON: Complete Robust Preference Data for Robust Alignment of Large Language Models
This paper addresses the challenges of aligning large language models (LLMs) with human values via preference learning (PL), with a focus on the issues of incomplete and corrupted data in preference datasets.
PENTATRON: PErsonalized coNText-Aware Transformer for Retrieval-based cOnversational uNderstanding
Conversational understanding is an integral part of modern intelligent devices.
ScaLA: Accelerating Adaptation of Pre-Trained Transformer-Based Language Models via Efficient Large-Batch Adversarial Noise
In recent years, large pre-trained Transformer-based language models have led to dramatic improvements in many natural language understanding tasks.
ScaLA: Speeding-Up Fine-tuning of Pre-trained Transformer Networks via Efficient and Scalable Adversarial Perturbation
To address this challenge, we propose ScaLA, a scalable and robust method for large-batch optimization of transformer networks via adversarial perturbation.
