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Found 12 papers, 7 papers with code
Only a Little to the Left: A Theory-grounded Measure of Political Bias in Large Language Models
Prompt-based language models like GPT4 and LLaMa have been used for a wide variety of use cases such as simulating agents, searching for information, or for content analysis.
R.U.Psycho? Robust Unified Psychometric Testing of Language Models
In this paper, we present R. U. Psycho, a framework for designing and running robust and reproducible psychometric experiments on generative language models that requires limited coding expertise.
Extracting Affect Aggregates from Longitudinal Social Media Data with Temporal Adapters for Large Language Models
We focus our analysis on the beginning of the COVID-19 pandemic that had a strong impact on public opinion and collective emotions.
Computational analysis of US Congressional speeches reveals a shift from evidence to intuition
Pursuit of honest and truthful decision-making is crucial for governance and accountability in democracies.
LEIA: Linguistic Embeddings for the Identification of Affect
We present LEIA, a model for emotion identification in text that has been trained on a dataset of more than 6 million posts with self-annotated emotion labels for happiness, affection, sadness, anger, and fear.
Neural Lumped Parameter Differential Equations with Application in Friction-Stir Processing
Lumped parameter methods aim to simplify the evolution of spatially-extended or continuous physical systems to that of a "lumped" element representative of the physical scales of the modeled system.
LEXpander: applying colexification networks to automated lexicon expansion
Recent approaches to text analysis from social media and other corpora rely on word lists to detect topics, measure meaning, or to select relevant documents.
Detecting potentially harmful and protective suicide-related content on twitter: A machine learning approach
The two deep learning models achieved the best performance in two classification tasks: In the first task, we classified six main content categories, including personal stories about either suicidal ideation and attempts or coping, calls for action intending to spread either problem awareness or prevention-related information, reporting of suicide cases, and other tweets irrelevant to these categories.
Improving accuracy and speeding up Document Image Classification through parallel systems
This paper presents a study showing the benefits of the EfficientNet models compared with heavier Convolutional Neural Networks (CNNs) in the Document Classification task, essential problem in the digitalization process of institutions.
Ranked #1 on
Multi-Modal Document Classification
on Tobacco-3482
A Streaming On-Device End-to-End Model Surpassing Server-Side Conventional Model Quality and Latency
Thus far, end-to-end (E2E) models have not been shown to outperform state-of-the-art conventional models with respect to both quality, i. e., word error rate (WER), and latency, i. e., the time the hypothesis is finalized after the user stops speaking.
Mixed Precision Training
Using this approach, we can reduce the memory consumption of deep learning models by nearly 2x.
Women Through the Glass Ceiling: Gender Asymmetries in Wikipedia
Contributing to the writing of history has never been as easy as it is today thanks to Wikipedia, a community-created encyclopedia that aims to document the world's knowledge from a neutral point of view.
Social and Information Networks
