Unprompted, LLM Agents can strategically deceive users when put under pressure

Nov 17, 2023 ·

This article presents a new approach to natural language processing using deep learning. The approach is based on the transformer architecture and uses a self-attention mechanism to process text. This self-attention mechanism allows the model to learn long-range dependencies between two words or phrases within a sentence. The approach also uses a novel technique for learning from textual data, called “contextual prediction”. This technique trains the model to predict what words will come next in a sentence, given its context. The proposed model is evaluated on several standard benchmark datasets, including the Stanford Question Answering Dataset (SQuAD), and it achieves state-of-the-art performance. The results show that the proposed model is capable of accurately understanding and modeling the underlying semantics of a given sentence. In addition, the results demonstrate the effectiveness of the self-attention mechanism and contextual prediction for learning from textual data. This article provides the first comprehensive study of natural language processing using the transformer architecture and demonstrates the potential of this approach for an effective and efficient way to process text.