23 citations · 48 across the 15 of their papers we have counts for
15 papers
The Subjectivity of Respect in Police Traffic Stops: Modeling Community Perspectives in Body-Worn Camera Footage
Preni Golazizian, Elnaz Rahmati, Jackson Trager +17
Traffic stops are among the most frequent police-civilian interactions, and body-worn cameras (BWCs) provide a unique record of how these encounters unfold. Respect is a central di…
Authors Should Label Their Own Documents
Marcus Ma, Cole Johnson, Nolan Bridges +3
Third-party annotation is the status quo for labeling text, but egocentric information such as sentiment and belief can at best only be approximated by a third-person proxy. We int…
Semantic F1 Scores: Fair Evaluation Under Fuzzy Class Boundaries
Georgios Chochlakis, Jackson Trager, Vedant Jhaveri +3
We propose Semantic F1 Scores, novel evaluation metrics for subjective or fuzzy multi-label classification that quantify semantic relatedness between predicted and gold labels. Unl…
Large Language Models Do Multi-Label Classification Differently
Marcus Ma, Georgios Chochlakis, Niyantha Maruthu Pandiyan +2
Multi-label classification is prevalent in real-world settings, but the behavior of Large Language Models (LLMs) in this setting is understudied. We investigate how autoregressive…
Humans Hallucinate Too: Language Models Identify and Correct Subjective Annotation Errors With Label-in-a-Haystack Prompts
Georgios Chochlakis, Peter Wu, Arjun Bedi +3
Modeling complex subjective tasks in Natural Language Processing, such as recognizing emotion and morality, is considerably challenging due to significant variation in human annota…
Larger Language Models Don't Care How You Think: Why Chain-of-Thought Prompting Fails in Subjective Tasks
Georgios Chochlakis, Niyantha Maruthu Pandiyan, Kristina Lerman +1
In-Context Learning (ICL) in Large Language Models (LLM) has emerged as the dominant technique for performing natural language tasks, as it does not require updating the model para…
Aggregation Artifacts in Subjective Tasks Collapse Large Language Models' Posteriors
Georgios Chochlakis, Alexandros Potamianos, Kristina Lerman +1
In-context Learning (ICL) has become the primary method for performing natural language tasks with Large Language Models (LLMs). The knowledge acquired during pre-training is cruci…
The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition
Georgios Chochlakis, Alexandros Potamianos, Kristina Lerman +1
In-context Learning (ICL) has emerged as a powerful paradigm for performing natural language tasks with Large Language Models (LLM) without updating the models' parameters, in cont…
Community-Informed AI Models for Police Accountability
Benjamin A. T. Graham, Lauren Brown, Georgios Chochlakis +13
Face-to-face interactions between police officers and the public affect both individual well-being and democratic legitimacy. Many government-public interactions are captured on vi…
Socio-Linguistic Characteristics of Coordinated Inauthentic Accounts
Keith Burghardt, Ashwin Rao, Siyi Guo +6
Online manipulation is a pressing concern for democracies, but the actions and strategies of coordinated inauthentic accounts, which have been used to interfere in elections, are n…
Using Emotion Embeddings to Transfer Knowledge Between Emotions, Languages, and Annotation Formats
Georgios Chochlakis, Gireesh Mahajan, Sabyasachee Baruah +3
The need for emotional inference from text continues to diversify as more and more disciplines integrate emotions into their theories and applications. These needs include inferrin…
Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion
Georgios Chochlakis, Gireesh Mahajan, Sabyasachee Baruah +3
Detecting emotions expressed in text has become critical to a range of fields. In this work, we investigate ways to exploit label correlations in multi-label emotion recognition mo…
VAuLT: Augmenting the Vision-and-Language Transformer for Sentiment Classification on Social Media
Georgios Chochlakis, Tejas Srinivasan, Jesse Thomason +1
We propose the Vision-and-Augmented-Language Transformer (VAuLT). VAuLT is an extension of the popular Vision-and-Language Transformer (ViLT), and improves performance on vision-an…
CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks
Tejas Srinivasan, Ting-Yun Chang, Leticia Leonor Pinto Alva +3
Current state-of-the-art vision-and-language models are evaluated on tasks either individually or in a multi-task setting, overlooking the challenges of continually learning (CL) t…
End-to-end Generative Zero-shot Learning via Few-shot Learning
Georgios Chochlakis, Efthymios Georgiou, Alexandros Potamianos
Contemporary state-of-the-art approaches to Zero-Shot Learning (ZSL) train generative nets to synthesize examples conditioned on the provided metadata. Thereafter, classifiers are…