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Found 12 papers, 9 papers with code
Enhancing Character-Level Understanding in LLMs through Token Internal Structure Learning
Tokenization techniques such as Byte-Pair Encoding (BPE) and Byte-Level BPE (BBPE) have significantly improved the computational efficiency and vocabulary representation stability of large language models (LLMs) by segmenting text into tokens.
ShieldGemma: Generative AI Content Moderation Based on Gemma
We present ShieldGemma, a comprehensive suite of LLM-based safety content moderation models built upon Gemma2.
Assessing Model Generalization in Vicinity
This paper evaluates the generalization ability of classification models on out-of-distribution test sets without depending on ground truth labels.
Towards Hierarchical Multi-Agent Workflows for Zero-Shot Prompt Optimization
Specifically, we include a hierarchy of LLMs, first constructing a prompt with precise instructions and accurate wording in a hierarchical manner, and then using this prompt to generate the final answer to the user query.
Optimizing Calibration by Gaining Aware of Prediction Correctness
For example, for a narrow misclassification, a calibrator trained by the CE loss often produces high confidence on the wrongly predicted class (e. g., a test sample is wrongly classified and its softmax score on the ground truth class is around 0. 4), which is undesirable.
Harm Amplification in Text-to-Image Models
Text-to-image (T2I) models have emerged as a significant advancement in generative AI; however, there exist safety concerns regarding their potential to produce harmful image outputs even when users input seemingly safe prompts.
An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection
However, existing deep learning based models struggle to simultaneously achieve the requirements of both high precision and real-time performance.
How to Synthesize a Large-Scale and Trainable Micro-Expression Dataset?
To this end, we develop a protocol to automatically synthesize large scale MiE training data that allow us to train improved recognition models for real-world test data.
Synthetic Data Are as Good as the Real for Association Knowledge Learning in Multi-object Tracking
We show that compared with real data, association knowledge obtained from synthetic data can achieve very similar performance on real-world test sets without domain adaption techniques.
Boosting Semi-Supervised Face Recognition with Noise Robustness
This paper presents an effective solution to semi-supervised face recognition that is robust to the label noise aroused by the auto-labelling.
Semi-Siamese Training for Shallow Face Learning
Extensive experiments on various benchmarks of face recognition show the proposed method significantly improves the training, not only in shallow face learning, but also for conventional deep face data.
