1,721,033 research outputs found
On Efficient Language and Vision Assistants for Visually-Situated Natural Language Understanding: What Matters in Reading and Reasoning
No full textTowards Reliable and Practical Phishing Detection
No full textAs the prevalence of phishing attacks continues to rise, there is an increasing demand for
more robust detection technologies. With recent advances in AI, we discuss how to construct a reliable and practical phishing detection
system using language models. For this system, we introduce the first large-scale Korean
dataset for phishing detection, encompassing
six types of phishing attacks. We consider multiple factors for building a real-time detection
system for edge devices, such as model size,
Speech-To-Text quality, split length, training
technique and multi-task learning. We evaluate the model’s ability twofold: in-domain, and
unseen attack detection performance which is
referred to as zero-day performance. Additionally, we demonstrate the importance of accurate comparison groups and evaluation datasets,
showing that voice phishing detection performs
reasonably well while smishing detection remains challenging. Both the dataset and the
trained model will be available upon request
Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners
No full textHierarchical Deconstruction of LLM Reasoning: A Graph-Based Framework for Analyzing Knowledge Utilization
No full text
Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models
No full textGradient Ascent Post-training Enhances Language Model Generalization
No full textIn this work, we empirically show that updating pretrained LMs (350M, 1.3B, 2.7B) with just a few steps of Gradient Ascent Post-training (GAP) on random, unlabeled text corpora enhances its zero-shot generalization capabilities across diverse NLP tasks. Specifically, we show that GAP can allow LMs to become comparable to 2-3x times larger LMs across 12 different NLP tasks. We also show that applying GAP on out-of-distribution corpora leads to the most reliable performance improvements. Our findings indicate that GAP can be a promising method for improving the generalization capability of LMs without any task-specific fine-tunin
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