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Found 17 papers, 6 papers with code
The Shrinking Landscape of Linguistic Diversity in the Age of Large Language Models
Language is far more than a communication tool.
Evaluating Creativity and Deception in Large Language Models: A Simulation Framework for Multi-Agent Balderdash
This paper introduces a simulation framework utilizing the game Balderdash to evaluate both the creativity and logical reasoning of LLMs.
Secret Keepers: The Impact of LLMs on Linguistic Markers of Personal Traits
However, with the increasing adoption of Large Language Models (LLMs) as writing assistants in everyday writing, a critical question emerges: are authors' linguistic patterns still predictive of their personal traits when LLMs are involved in the writing process?
Cost-Efficient Subjective Task Annotation and Modeling through Few-Shot Annotator Adaptation
In subjective NLP tasks, where a single ground truth does not exist, the inclusion of diverse annotators becomes crucial as their unique perspectives significantly influence the annotations.
A Multi-Perspective Machine Learning Approach to Evaluate Police-Driver Interaction in Los Angeles
Interactions between the government officials and civilians affect public wellbeing and the state legitimacy that is necessary for the functioning of democratic society.
Towards a Unified Framework for Adaptable Problematic Content Detection via Continual Learning
Detecting problematic content, such as hate speech, is a multifaceted and ever-changing task, influenced by social dynamics, user populations, diversity of sources, and evolving language.
Social-Group-Agnostic Word Embedding Debiasing via the Stereotype Content Model
Existing word embedding debiasing methods require social-group-specific word pairs (e. g., "man"-"woman") for each social attribute (e. g., gender), which cannot be used to mitigate bias for other social groups, making these methods impractical or costly to incorporate understudied social groups in debiasing.
The Moral Foundations Reddit Corpus
Moral framing and sentiment can affect a variety of online and offline behaviors, including donation, pro-environmental action, political engagement, and even participation in violent protests.
Hate Speech Classifiers Learn Human-Like Social Stereotypes
Social stereotypes negatively impact individuals' judgements about different groups and may have a critical role in how people understand language directed toward minority social groups.
Improving Counterfactual Generation for Fair Hate Speech Detection
By applying logit pairing to equalize outcomes on the restricted set of counterfactuals for each instance, we improve fairness metrics while preserving model performance on hate speech detection.
Fair Hate Speech Detection through Evaluation of Social Group Counterfactuals
Counterfactual token fairness for a mentioned social group evaluates the model's predictions as to whether they are the same for (a) the actual sentence and (b) a counterfactual instance, which is generated by changing the mentioned social group in the sentence.
Contextualizing Hate Speech Classifiers with Post-hoc Explanation
Hate speech classifiers trained on imbalanced datasets struggle to determine if group identifiers like "gay" or "black" are used in offensive or prejudiced ways.
Reporting the Unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes
Official reports of hate crimes in the US are under-reported relative to the actual number of such incidents.
Multilingual Entity, Relation, Event and Human Value Extraction
This paper demonstrates a state-of-the-art end-to-end multilingual (English, Russian, and Ukrainian) knowledge extraction system that can perform entity discovery and linking, relation extraction, event extraction, and coreference.
Acquiring Background Knowledge to Improve Moral Value Prediction
In this paper, we address the problem of detecting expressions of moral values in tweets using content analysis.
