E. Liscio
12 records found
1
This study investigates the effectiveness of Large Language Models (LLMs) in identifying and classifying subjective arguments within deliberative discourse. Using data from a Participatory Value Evaluation (PVE) conducted in the Netherlands, this research introduces an annotation
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This paper investigates the use of Large Language Models (LLMs) for automatic detection of subjective values in argument statements in public discourse. Understanding the underlying values of argument statements could enhance public discussions and potentially lead to better outc
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Public deliberations play a crucial role in democratic systems. However, the unstructured nature of deliberations leads to challenges for moderators to analyze the large volume of data produced. This paper aims to solve this challenge by automatically identifying subjective topic
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Using Large Language Models to Detect Deliberative Elements in Public Discourse
Detecting Subjective Emotions in Public Discourse
In order to tackle topics such as climate change together with the population, public discourse should be scaled up. This discourse should be mediated as it makes it more likely that people understand each other and change their point of view. To help the mediator with this task,
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Decoding Sentiment with Large Language Models
Comparing Prompting Strategies Across Hard, Soft, and Subjective Label Scenarios
This study evaluates the performance of different sentiment analysis methods in the context of public deliberation, focusing on hard-, soft-, and subjective-label scenarios to answer the research question: ``can a Large Language Model detect subjective sentiment of statements wit
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Moral values influence humans in decision-making. Pluralist moral philosophers argue that human morality can be represented by a finite number of moral values, respecting the differences in moral views. Recent advancements in NLP show that language models retain a discernible lev
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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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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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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 ...
Understanding personal values is a crucial aspect that can facilitate the collaboration between AI and humans. Nonetheless, the implementation of collaborative agents in real life greatly depends on the amount of trust that is built in their relationship with people. In order to
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Moral values are instrumental in understanding people's beliefs and behaviors. Estimating such values from text would facilitate the interaction between humans and computers. To date, no comparison between NLP models for predicting moral values from text exists. This paper addres
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Personal moral values represent the motivation behind individuals' actions and opinions. Understanding these values is helpful both in predicting individuals' actions, such as violent protests, and building AI that can better collaborate with humans. Predicting moral values is a
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