It is the subfield of computer science that focuses on creating systems capable of intelligent behavior, including problem solving, learning, adaptation, perception, and language understanding.
This study introduces a new DQN-based Cross-Stream Crisis event Summarizer that addresses the challenges in summarizing multiple disaster-relevant data streams. It uses Deep Reinforcement Learning (DRL) to retrieve relevant texts…
Continue readingThe article discusses the role of cognitive biases in decision-making and the need for automated detection of these biases. The research explores the use of conversational agents as a tool…
Continue readingThe article discusses the use of Supervised Finetuning (SFT) on instruction datasets for improving the performance of large language models (LLMs). However, the high cost of annotation for quality responses…
Continue readingThe article discusses a new method for weight optimization in fully-connected feed-forward neural networks, which uses least squares (LS) methodology instead of the commonly used back-propagation (BP). This method allows…
Continue readingThis research paper discusses the use of natural language processing (NLP) and AI models for mental health analysis in social media posts, specifically focusing on detecting low self-esteem in Reddit…
Continue readingThe research reframes statutory reasoning as an analogy task, increasing the dataset size and interpretability. The statutory reasoning refers to the application of legislative provisions to a series of case…
Continue readingThe paper explores the ability of language models (LMs) to express uncertainties when answering questions. It was found that LMs often fail to express uncertainties, even when producing incorrect responses….
Continue readingThis article by Alexandra DeLucia et al. addresses the challenge of creating unique personas for dialogue agents, particularly in non-real-world settings such as fantasy. Existing models, mainly trained on PersonaChat,…
Continue readingThe research paper explores the notion of ‘easy-to-hard generalization’ in language models. It addresses the problem of training models on hard data which is often difficult to label correctly. The…
Continue readingThe article presents a framework, ARCANE, for generating synthetic datasets to improve models predicting pedestrian intentions, crucial for autonomous driving. The lack of diverse datasets with crossing and non-crossing scenarios…
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