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Token Trails: Navigating Contextual Depths in Conversational AI with ChatLLM
Apr 03, 2024
Conversational modeling using Large Language Models (LLMs) requires a nuanced understanding of context to generate coherent and contextually relevant responses. In this paper, we present Token Trails, a novel approach that leverages token-type embeddings to navigate the intricate contextual nuances within conversations. Our framework utilizes token-type embeddings to distinguish between user utterances and bot responses, facilitating the generation of context-aware replies. Through comprehensive experimentation and evaluation, we demonstrate the effectiveness of Token Trails in improving conversational understanding and response generation, achieving state-of-the-art performance. Our results highlight the significance of contextual modeling in conversational AI and underscore the promising potential of Token Trails to advance the field, paving the way for more sophisticated and contextually aware chatbot interactions.
Impact Learning: A Learning Method from Features Impact and Competition
Nov 04, 2022
Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of wellknown machine learning algorithms have been developed for use in the field of computer science to analyze data. This paper introduced a new machine learning algorithm called impact learning. Impact learning is a supervised learning algorithm that can be consolidated in both classification and regression problems. It can furthermore manifest its superiority in analyzing competitive data. This algorithm is remarkable for learning from the competitive situation and the competition comes from the effects of autonomous features. It is prepared by the impacts of the highlights from the intrinsic rate of natural increase (RNI). We, moreover, manifest the prevalence of the impact learning over the conventional machine learning algorithm.