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Found 12 papers, 8 papers with code
DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformation
Disinformation has become a serious problem on social media.
PaLM: Scaling Language Modeling with Pathways
To further our understanding of the impact of scale on few-shot learning, we trained a 540-billion parameter, densely activated, Transformer language model, which we call Pathways Language Model PaLM.
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Representation Learning for Resource-Constrained Keyphrase Generation
State-of-the-art keyphrase generation methods generally depend on large annotated datasets, limiting their performance in domains with limited annotated data.
Socially Aware Bias Measurements for Hindi Language Representations
Language representations are efficient tools used across NLP applications, but they are strife with encoded societal biases.
Harms of Gender Exclusivity and Challenges in Non-Binary Representation in Language Technologies
Gender is widely discussed in the context of language tasks and when examining the stereotypes propagated by language models.
What do Bias Measures Measure?
To address this gap, this work presents a comprehensive survey of existing bias measures in NLP as a function of the associated NLP tasks, metrics, datasets, and social biases and corresponding harms.
VERB: Visualizing and Interpreting Bias Mitigation Techniques for Word Representations
To aid this, we present Visualization of Embedding Representations for deBiasing system ("VERB"), an open-source web-based visualization tool that helps the users gain a technical understanding and visual intuition of the inner workings of debiasing techniques, with a focus on their geometric properties.
The Geometry of Distributed Representations for Better Alignment, Attenuated Bias, and Improved Interpretability
High-dimensional representations for words, text, images, knowledge graphs and other structured data are commonly used in different paradigms of machine learning and data mining.
OSCaR: Orthogonal Subspace Correction and Rectification of Biases in Word Embeddings
Language representations are known to carry stereotypical biases and, as a result, lead to biased predictions in downstream tasks.
On Measuring and Mitigating Biased Inferences of Word Embeddings
Word embeddings carry stereotypical connotations from the text they are trained on, which can lead to invalid inferences in downstream models that rely on them.
Attenuating Bias in Word Vectors
Word vector representations are well developed tools for various NLP and Machine Learning tasks and are known to retain significant semantic and syntactic structure of languages.
Closed Form Word Embedding Alignment
We develop a family of techniques to align word embeddings which are derived from different source datasets or created using different mechanisms (e. g., GloVe or word2vec).
