Quickly access the latest research papers on large language models from arXiv.
Papers and research related to Large Language Model
This paper identifies critical limitations in the reasoning robustness of large language models (LLMs), revealing performance drops on novel data and introducing a benchmark, Math-RoB, to assess these challenges.
This research explores the transferability of safety interventions between Large Language Models (LLMs) by utilizing shared activation spaces, demonstrating effective methods for backdoor removal and harmful prompt refusal.
This paper presents a Vision Language Model (VLM)-based Multi-Agent System designed to automate the CAD modeling process, enhancing collaborative design in industrial and hobbyist contexts.
AgentSafe is a framework designed to enhance the security of Large Language Model-based multi-agent systems by implementing hierarchical data management and memory protection strategies to mitigate security threats.
The MathMistake Checker is an innovative system that automates the identification of mistakes in step-by-step mathematical problem-solving, leveraging advanced LLM capabilities to enhance grading and learning experiences.
The paper presents VirtualXAI, a framework that enhances explainability in AI by integrating quantitative and qualitative assessments through GPT-generated personas, addressing challenges in evaluating XAI methods.
KidneyTalk-open is a no-code desktop system designed for secure deployment of large language models (LLMs) in kidney disease management, integrating medical documentation for enhanced clinical decision support.
The paper presents a bipartite mutual information scaling law that governs long-range dependencies in language modeling, establishing a theoretical foundation for enhancing long-context language models.
This paper emphasizes the need to shift focus in long-context LLM research from input processing to the generation of long-form outputs, addressing a significant gap in current capabilities.
This research paper examines how large language models (LLMs) utilize in-context learning (ICL) to perform structured reasoning consistent with Bayesian principles, particularly through biased coin flip scenarios.
This research paper presents universal scaling laws for hyperparameter optimization in Large Language Models (LLMs), revealing optimal relationships for learning rates and batch sizes across various configurations.
This research paper addresses the need for watermarking techniques to detect misuse of open-source large language models (LLMs), focusing on intellectual property violations and usage violations.