Practices for Governing Agentic AI Systems
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Large language models (LLMs) have made impressive advancements in generating coherent text for various activities and domains, including grammatical error correction (GEC), text simplification, paraphrasing, and style transfer. One of the emerging skills of LLMs is their ability to generalize and perform tasks that they have never seen before. To achieve this, LLMs are fine-tuned…
Person Re-identification (Person Re-ID) in Machine Learning uses deep learning models like convolutional neural networks to recognize and track individuals across different camera views, holding promise for surveillance and public safety but raising significant privacy concerns. The technology’s capacity to track people across locations increases surveillance and security risks, along with potential privacy issues like…
Large Language Models (LLMs) signify a remarkable advance in natural language processing and artificial intelligence. These models, exemplified by their ability to understand and generate human language, have revolutionized numerous applications, from automated writing to translation. However, their complexity and potential for misuse, such as spreading misinformation or biased content, have raised significant concerns about…
In recent times, Large Language Models (LLMs) have gained popularity for their ability to respond to user queries in a more human-like manner, accomplished through reinforcement learning. However, aligning these LLMs with human preferences in reinforcement learning from human feedback (RLHF) can lead to a phenomenon known as reward hacking. This occurs when LLMs exploit…
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Large language models (LLMs) based on transformer architectures have emerged in recent years. Models such as Chat-GPT and LLaMA-2 demonstrate how the parameters of LLMs have rapidly increased, ranging from several billion to tens of trillions. Although LLMs are very good generators, they have trouble with inference delay since there is a lot of computing…