Based on Data Balancing and Model Improvement for Multi-Label Sentiment Classification Performance Enhancement

This study evaluates the effectiveness of various large language models (LLMs) in restoring diacritics in Romanian texts, a crucial task for text processing in languages with rich diacritical marks. The models tested include OpenAI's GPT-3.5, GPT-4, Google's Gemini 1.0 Pro, and Meta's Llama family, among others. Results indicate that GPT-4o achieves high accuracy in diacritic restoration, outperforming a neutral baseline, while other models show variability. The findings emphasize the importance of model architecture, training data, and prompt design in enhancing natural language processing to…

Recent advancements in Large Language Models (LLMs) have sparked interest in automating cybersecurity penetration testing workflows, promising faster and more consistent vulnerability assessments for enterprise systems. Current LLM agents often rely on self-guided reasoning, which can lead to inaccuracies and unproductive actions. This work proposes a guided reasoning pipeline that utilizes a deterministic task tree based on the MITRE ATT&CK Matrix, ensuring the LLM's reasoning is anchored in established penetration testing methodologies.

A new framework for Named Entity Recognition (NER) has been proposed to enhance the performance of generative large language models (LLMs) like GPT-4. While these models excel at generating entities through semantic pattern matching, they often lack a robust reasoning mechanism, leading to suboptimal outcomes, particularly in low-resource scenarios. The proposed framework shifts the paradigm from implicit pattern matching to explicit reasoning, involving three stages: Chain of Thought (CoT) generation, CoT tuning, and reasoning enhancement, ultimately aiming to improve NER accuracy.

The article discusses the growing interest in world models, particularly in the context of advancements in multimodal large language models like GPT-4 and video generation models such as Sora. It provides a comprehensive review of the literature on world models, which serve to either understand the current state of the world or predict future dynamics. The review categorizes world models based on their functions: constructing internal representations and predicting future states, with applications in generative games, autonomous driving, robotics, and social simulacra.