A Reasoning Paradigm for Named Entity Recognition

The article presents a novel training strategy called Curriculum-based Relative Policy Optimization (CuRPO) aimed at improving Visual Grounding tasks. It highlights the limitations of Chain-of-Thought (CoT) prompting, particularly when outputs become lengthy or complex, which can degrade performance. The study reveals that simply increasing dataset size does not guarantee better results due to varying complexities. CuRPO utilizes CoT length and generalized Intersection over Union (gIoU) rewards to structure training data progressively from simpler to more challenging examples, demonstrating ef…

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.

The article introduces Gen-ViRe, a new benchmark for generative visual reasoning that addresses the limitations of current models in simulating real-world dynamics. While Chain-of-Thought (CoT) prompting has advanced symbolic reasoning in large language models (LLMs), it is limited to discrete text. Gen-ViRe aims to evaluate Chain-of-Frames (CoF) reasoning, which translates thought into visual sequences, thereby assessing cognitive abilities in multi-step planning and abstract reasoning. This benchmark seeks to fill a gap in understanding model capabilities.

The paper titled 'From Perception to Reasoning: Deep Thinking Empowers Multimodal Large Language Models' discusses the advancements in Multimodal Large Language Models (MLLMs) and their reasoning capabilities. It highlights the challenges faced by existing models, such as opaque reasoning paths and limited generalization. The study emphasizes the potential of Chain-of-Thought (CoT) reasoning to enhance transparency and interpretability in MLLMs, proposing a systematic review of Multimodal Chain-of-Thought (MCoT) methods to improve reasoning capabilities.

The article discusses advancements in multi-label sentiment classification, a key area in natural language processing that identifies various emotions in a single text. Existing datasets, such as GoEmotions, often face class imbalance issues, negatively affecting model performance. To combat this, a balanced multi-label sentiment dataset was created by merging GoEmotions data, emotion-labeled samples from Sentiment140, and manually annotated texts generated by GPT-4 mini. An improved classification model was developed using FastText embeddings, convolutional layers, bidirectional LSTM, and an …

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…

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.