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Found 23 papers, 16 papers with code
Expected Validation Performance and Estimation of a Random Variable’s Maximum
We find that the two biased estimators lead to the fewest incorrect conclusions, which hints at the importance of minimizing variance and MSE.
When it Rains, it Pours: Modeling Media Storms and the News Ecosystem
Most events in the world receive at most brief coverage by the news media.
You don't need a personality test to know these models are unreliable: Assessing the Reliability of Large Language Models on Psychometric Instruments
The versatility of Large Language Models (LLMs) on natural language understanding tasks has made them popular for research in social sciences.
Substitution-based Semantic Change Detection using Contextual Embeddings
Measuring semantic change has thus far remained a task where methods using contextual embeddings have struggled to improve upon simpler techniques relying only on static word vectors.
Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words
Cosine similarity of contextual embeddings is used in many NLP tasks (e. g., QA, IR, MT) and metrics (e. g., BERTScore).
Modular Domain Adaptation
Off-the-shelf models are widely used by computational social science researchers to measure properties of text, such as sentiment.
Whose Language Counts as High Quality? Measuring Language Ideologies in Text Data Selection
Language models increasingly rely on massive web dumps for diverse text data.
Expected Validation Performance and Estimation of a Random Variable's Maximum
We find that the two biased estimators lead to the fewest incorrect conclusions, which hints at the importance of minimizing variance and MSE.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
The Values Encoded in Machine Learning Research
We present extensive textual evidence and identify key themes in the definitions and operationalization of these values.
Detecting Stance in Media on Global Warming
We release our stance dataset, model, and lexicons of framing devices for future work on opinion-framing and the automatic detection of GW stance.
Causal Effects of Linguistic Properties
Second, in practice, we only have access to noisy proxies for the linguistic properties of interest -- e. g., predictions from classifiers and lexicons.
With Little Power Comes Great Responsibility
Despite its importance to experimental design, statistical power (the probability that, given a real effect, an experiment will reject the null hypothesis) has largely been ignored by the NLP community.
On Consequentialism and Fairness
In this paper we provide a consequentialist critique of common definitions of fairness within machine learning, as well as a machine learning perspective on consequentialism.
Show Your Work: Improved Reporting of Experimental Results
Research in natural language processing proceeds, in part, by demonstrating that new models achieve superior performance (e. g., accuracy) on held-out test data, compared to previous results.
The Risk of Racial Bias in Hate Speech Detection
We investigate how annotators{'} insensitivity to differences in dialect can lead to racial bias in automatic hate speech detection models, potentially amplifying harm against minority populations.
Variational Pretraining for Semi-supervised Text Classification
We accompany this paper with code to pretrain and use VAMPIRE embeddings in downstream tasks.
Deep Weighted Averaging Classifiers
Recent advances in deep learning have achieved impressive gains in classification accuracy on a variety of types of data, including images and text.
The Importance of Calibration for Estimating Proportions from Annotations
Estimating label proportions in a target corpus is a type of measurement that is useful for answering certain types of social-scientific questions.
Neural Models for Documents with Metadata
Most real-world document collections involve various types of metadata, such as author, source, and date, and yet the most commonly-used approaches to modeling text corpora ignore this information.
Friendships, Rivalries, and Trysts: Characterizing Relations between Ideas in Texts
Combining two statistics --- cooccurrence within documents and prevalence correlation over time --- our approach reveals a number of different ways in which ideas can cooperate and compete.
