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Found 30 papers, 10 papers with code
Automatic Identification of 5C Vaccine Behaviour on Social Media
Monitoring vaccine behaviour through social media can guide health policy.
How Do Experts Make Sense of Integrated Process Models?
By studying expert process workers engaged in tasks based on integrated modeling of business processes and rules, we provide insights that pave the way for a better understanding of sensemaking practices and improved development of business process and business rule integration approaches.
LLM-based Semantic Augmentation for Harmful Content Detection
Recent advances in large language models (LLMs) have demonstrated strong performance on simple text classification tasks, frequently under zero-shot settings.
Spotting Persuasion: A Low-cost Model for Persuasion Detection in Political Ads on Social Media
In the realm of political advertising, persuasion operates as a pivotal element within the broader framework of propaganda, exerting profound influences on public opinion and electoral outcomes.
Leveraging Semantic Type Dependencies for Clinical Named Entity Recognition
Previous work on clinical relation extraction from free-text sentences leveraged information about semantic types from clinical knowledge bases as a part of entity representations.
Are Large Language Models Good Data Preprocessors?
High-quality textual training data is essential for the success of multimodal data processing tasks, yet outputs from image captioning models like BLIP and GIT often contain errors and anomalies that are difficult to rectify using rule-based methods.
Plan-Then-Execute: An Empirical Study of User Trust and Team Performance When Using LLM Agents As A Daily Assistant
Our findings demonstrate that LLM agents can be a double-edged sword -- (1) they can work well when a high-quality plan and necessary user involvement in execution are available, and (2) users can easily mistrust the LLM agents with plans that seem plausible.
The Impact of Persona-based Political Perspectives on Hateful Content Detection
While pretraining language models with politically diverse content has been shown to improve downstream task fairness, such approaches require significant computational resources often inaccessible to many researchers and organizations.
Exploring Wikipedia Gender Diversity Over Time $\unicode{x2013}$ The Wikipedia Gender Dashboard (WGD)
Wikipedia editors can make use of WGD to locate areas with marginalized genders in Wikipedia, and increase their efforts to produce more content providing coverage for those genders to achieve better gender equality in Wikipedia.
SubData: Bridging Heterogeneous Datasets to Enable Theory-Driven Evaluation of Political and Demographic Perspectives in LLMs
As increasingly capable large language models (LLMs) emerge, researchers have begun exploring their potential for subjective tasks.
Mapping and Influencing the Political Ideology of Large Language Models using Synthetic Personas
The analysis of political biases in large language models (LLMs) has primarily examined these systems as single entities with fixed viewpoints.
Perception of Visual Content: Differences Between Humans and Foundation Models
Human-annotated content is often used to train machine learning (ML) models.
Natural Language Processing for the Legal Domain: A Survey of Tasks, Datasets, Models, and Challenges
Natural Language Processing (NLP) is revolutionising the way legal professionals and laypersons operate in the legal field.
Optimizing LLMs with Direct Preferences: A Data Efficiency Perspective
Aligning the output of Large Language Models (LLMs) with human preferences (e. g., by means of reinforcement learning with human feedback, or RLHF) is essential for ensuring their effectiveness in real-world scenarios.
Personas with Attitudes: Controlling LLMs for Diverse Data Annotation
We present a novel approach for enhancing diversity and control in data annotation tasks by personalizing large language models (LLMs).
Fairness without Sensitive Attributes via Knowledge Sharing
While model fairness improvement has been explored previously, existing methods invariably rely on adjusting explicit sensitive attribute values in order to improve model fairness in downstream tasks.
Hate Speech Detection with Generalizable Target-aware Fairness
To counter the side effect brought by the proliferation of social media platforms, hate speech detection (HSD) plays a vital role in halting the dissemination of toxic online posts at an early stage.
Estimating Gender Completeness in Wikipedia
Gender imbalance in Wikipedia content is a known challenge which the editor community is actively addressing.
Identification of Regulatory Requirements Relevant to Business Processes: A Comparative Study on Generative AI, Embedding-based Ranking, Crowd and Expert-driven Methods
Organizations face the challenge of ensuring compliance with an increasing amount of requirements from various regulatory documents.
Data Bias Management
Due to the widespread use of data-powered systems in our everyday lives, concepts like bias and fairness gained significant attention among researchers and practitioners, in both industry and academia.
On the Impact of Data Quality on Image Classification Fairness
By adding noise to the original datasets, we can explore the relationship between the quality of the training data and the fairness of the output of the models trained on that data.
Perspectives on Large Language Models for Relevance Judgment
When asked, large language models (LLMs) like ChatGPT claim that they can assist with relevance judgments but it is not clear whether automated judgments can reliably be used in evaluations of retrieval systems.
Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?
Fact-checking is one of the effective solutions in fighting online misinformation.
Managing Bias in Human-Annotated Data: Moving Beyond Bias Removal
Due to the widespread use of data-powered systems in our everyday lives, the notions of bias and fairness gained significant attention among researchers and practitioners, in both industry and academia.
The Many Dimensions of Truthfulness: Crowdsourcing Misinformation Assessments on a Multidimensional Scale
We deploy a set of quality control mechanisms to ensure that the thousands of assessments collected on 180 publicly available fact-checked statements distributed over two datasets are of adequate quality, including a custom search engine used by the crowd workers to find web pages supporting their truthfulness assessments.
Can the Crowd Judge Truthfulness? A Longitudinal Study on Recent Misinformation about COVID-19
Our results show that: workers are able to detect and objectively categorize online (mis)information related to COVID-19; both crowdsourced and expert judgments can be transformed and aggregated to improve quality; worker background and other signals (e. g., source of information, behavior) impact the quality of the data.
The COVID-19 Infodemic: Can the Crowd Judge Recent Misinformation Objectively?
Misinformation is an ever increasing problem that is difficult to solve for the research community and has a negative impact on the society at large.
Proceedings of the KG-BIAS Workshop 2020 at AKBC 2020
The KG-BIAS 2020 workshop touches on biases and how they surface in knowledge graphs (KGs), biases in the source data that is used to create KGs, methods for measuring or remediating bias in KGs, but also identifying other biases such as how and which languages are represented in automatically constructed KGs or how personal KGs might incur inherent biases.
Can The Crowd Identify Misinformation Objectively? The Effects of Judgment Scale and Assessor's Background
Truthfulness judgments are a fundamental step in the process of fighting misinformation, as they are crucial to train and evaluate classifiers that automatically distinguish true and false statements.
FashionBrain Project: A Vision for Understanding Europe's Fashion Data Universe
A core business in the fashion industry is the understanding and prediction of customer needs and trends.
