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pro vyhledávání: '"68t50"'
Autor:
Glazkova, Anna, Zakharova, Olga
Large language models (LLMs) play a crucial role in natural language processing (NLP) tasks, improving the understanding, generation, and manipulation of human language across domains such as translating, summarizing, and classifying text. Previous s
Externí odkaz:
http://arxiv.org/abs/2411.14896
The fast advancement of Large Vision-Language Models (LVLMs) has shown immense potential. These models are increasingly capable of tackling abstract visual tasks. Geometric structures, particularly graphs with their inherent flexibility and complexit
Externí odkaz:
http://arxiv.org/abs/2411.14832
Large language models (LLMs) under-perform on low-resource languages due to limited training data. We present a method to efficiently collect text data for low-resource languages from the entire Common Crawl corpus. Our approach, UnifiedCrawl, filter
Externí odkaz:
http://arxiv.org/abs/2411.14343
Large Language Models are prone to off-topic misuse, where users may prompt these models to perform tasks beyond their intended scope. Current guardrails, which often rely on curated examples or custom classifiers, suffer from high false-positive rat
Externí odkaz:
http://arxiv.org/abs/2411.12946
The generation of complex, large-scale code projects using generative AI models presents challenges due to token limitations, dependency management, and iterative refinement requirements. This paper introduces the See-Saw generative mechanism, a nove
Externí odkaz:
http://arxiv.org/abs/2411.10861
Efficient deployment of resource-intensive transformers on edge devices necessitates cross-stack optimization. We thus study the interrelation between structured pruning and systolic acceleration, matching the size of pruned blocks with the systolic
Externí odkaz:
http://arxiv.org/abs/2411.10285
The prevalence of multi-modal content on social media complicates automated moderation strategies. This calls for an enhancement in multi-modal classification and a deeper understanding of understated meanings in images and memes. Although previous e
Externí odkaz:
http://arxiv.org/abs/2411.10480
Autor:
Sulimov, Daniil
Large Language Models (LLMs) are increasingly adopted for complex scientific text generation tasks, yet they often suffer from limitations in accuracy, consistency, and hallucination control. This thesis introduces a Parameter-Efficient Fine-Tuning (
Externí odkaz:
http://arxiv.org/abs/2411.06445
Natural Language Processing (NLP) operations, such as semantic sentiment analysis and text synthesis, may often impair users' privacy and demand significant on device computational resources. Centralized learning (CL) on the edge offers an alternativ
Externí odkaz:
http://arxiv.org/abs/2411.06291
Autor:
Formanek, Vojtech, Sotolar, Ondrej
A growing amount of literature critiques the current operationalizations of empathy based on loose definitions of the construct. Such definitions negatively affect dataset quality, model robustness, and evaluation reliability. We propose an empathy e
Externí odkaz:
http://arxiv.org/abs/2411.05777