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Found 9 papers, 3 papers with code
Open Source Conversational LLMs do not know most Spanish words
The growing interest in Large Language Models (LLMs) and in particular in conversational models with which users can interact has led to the development of a large number of open-source chat LLMs.
Establishing Vocabulary Tests as a Benchmark for Evaluating Large Language Models
Vocabulary tests, once a cornerstone of language modeling evaluation, have been largely overlooked in the current landscape of Large Language Models (LLMs) like Llama, Mistral, and GPT.
How many words does ChatGPT know? The answer is ChatWords
The benefits of ChatWords are illustrated with two case studies: evaluating the knowledge that ChatGPT has of the Spanish lexicon (taken from the official dictionary of the "Real Academia Espa\~nola") and of the words that appear in the Quixote, the well-known novel written by Miguel de Cervantes.
Launch Power Optimization for Dynamic Elastic Optical Networks over C+L Bands
We propose an algorithm for calculating the optimum launch power over the entire C+L bands by maximizing the cumulative link GSNR of a channel plan built upon multiple modulation formats, with application to dynamic EONs.
Playing with Words: Comparing the Vocabulary and Lexical Richness of ChatGPT and Humans
Therefore, further research is needed to understand how the use of ChatGPT and more broadly generative AI tools will affect the vocabulary and lexical richness in different types of text and languages.
Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet
Our results show that the quality and diversity of the generated images can degrade over time suggesting that incorporating AI-created data can have undesired effects on future versions of generative models.
Combining Generative Artificial Intelligence (AI) and the Internet: Heading towards Evolution or Degradation?
Therefore, future versions of generative AI tools will be trained with Internet data that is a mix of original and AI-generated data.
Android Malware Characterization using Metadata and Machine Learning Techniques
Android Malware has emerged as a consequence of the increasing popularity of smartphones and tablets.
On labeling Android malware signatures using minhashing and further classification with Structural Equation Models
Multi-scanner Antivirus systems provide insightful information on the nature of a suspect application; however there is often a lack of consensus and consistency between different Anti-Virus engines.
