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Artificial intelligence possesses large language models (LLMs) like GPT-4 that enable autonomous agents to carry out complex tasks within various environments with unprecedented accuracy. However, these agents still struggle to learn from failures, which is where the Exploration-based Trajectory Optimization (ETO) method comes in. This training introduced by the Allen Institute for AI; Peking University's…

The field of large language models (LLMs) has witnessed significant advances thanks to the introduction of State Space Models (SSMs). Offering a lower computational footprint, SSMs are seen as a welcome alternative. The recent development of DenseSSM represents a significant milestone in this regard. Designed by a team of researchers at Huawei's Noah's Ark Lab,…

The rapid development in Large Language Models (LLMs) has seen billion- or trillion-parameter models achieve impressive performance across multiple fields. However, their sheer scale poses real issues for deployment due to severe hardware requirements. The focus of current research has been on scaling models to improve performance, following established scaling laws. This, however, emphasizes the…

Large Language Models (LLMs) such as GPT-4 and Llama-2, while highly capable, require fine-tuning with specific data tailored to various business requirements. This process can expose the models to safety threats, most notably the Fine-tuning based Jailbreak Attack (FJAttack). The introduction of even a small number of harmful examples during the fine-tuning phase can drastically…