Contributed
NLP and reinforcement learning to generate morally aligned text
How does explainable models perform compared to black-box models
This paper evaluates the performance of an automated explainable model, Moral- Strength, to predict morality, or more pre- cisely Moral Foundations Theory (MFT) traits. MFT is a way to represent and divide morality into precise and detailed traits. This evaluation happens in ...
Natural Language Processing and Reinforcement Learning to Generate Morally Aligned Text
Comparing a moral agent to an optimally playing agent
Nowadays Large Language Models are becoming more and more prevalent in today's society. These models act without a sense of morality however. They only prioritize accomplishing their goal. Currently, little research has been done evaluating these models. The current state of the
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Natural Language Processing and Reinforcement Learning to Generate Morally
What is the optimal weight w to win the games while playing morally?
In our everyday life, people interact more and more with agents. However these agents often lack a moral sense and prioritize the accomplishment of the given task. In consequence, agents may unknowingly act immorally. Little research or progress has been done to endow agents with
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Balancing multidimensional morality and progression
Evaluating the tradeoff for artificial agents playing text-based games
Morality is a fundamental concept that guides humans in the decision-making process. Given the rise of large language models in society, it is necessary to ensure that they adhere to human principles, among which morality is of substantial importance. While research has been done
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What would Jiminy Cricket do?
A pluralist approach in generating and processing morally-aligned text
When making decisions, people are automatically guided by their moral compass. However, AI agents need to be conditioned in order to be steered towards moral behaviour. An environment that can be used to train and test agents is the Jiminy Cricket environment. The Jiminy Cricket
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Even though the abaility to recommend items in the long tail is one of the main strengths of recommendation systems, modern models still show decreased performance when recommending these niche items. Various bipartite and tripartite graph-based models have been proposed that are
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Recommender systems are an essential part of online businesses in today's day and age. They provide users with meaningful recommendations for items and products. A frequently occurring problem in recommender systems is known as the long-tail problem. It refers to a situation in w
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Recommender systems (RS) are a cornerstone for most online businesses that cater to a large customer base such as e-commerce, social network platforms and many others. RS's enable these platforms to provide tailor-made experiences to each of their customers by strategically utili
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Recommender Systems play a significant part in filtering and efficiently prioritizing relevant information to alleviate the information overload problem and maximize user engagement. Traditional recommender systems employ a static approach towards learning the user's preferences,
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