Grammar-Constrained Decoding for Structured NLP Tasks without Finetuning [PDF]
Conference on Empirical Methods in Natural Language Processing, 2023 Despite their impressive performance, large language models (LMs) still struggle with reliably generating complex output structures when not finetuned to follow the required output format exactly. To address this issue, grammar-constrained decoding (GCD)
Saibo Geng +3 more
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Automatic Classification of Sexism in Social Networks: An Empirical Study on Twitter Data
IEEE Access, 2020 During the last decade, hateful and sexist content towards women is being increasingly spread on social networks. The exposure to sexist speech has serious consequences to women's life and limits their freedom of speech.
Francisco Rodriguez-Sanchez +2 more
doaj +1 more source
Frontiers in Education, 2023 COPYRIGHT © 2023 Fuchs. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright ...
K. Fuchs
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Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP [PDF]
Transactions of the Association for Computational Linguistics, 2021 ⚠ This paper contains prompts and model outputs that are offensive in nature. When trained on large, unfiltered crawls from the Internet, language models pick up and reproduce all kinds of undesirable biases that can be found in the data: They often ...
Timo Schick +2 more
semanticscholar +1 more source
Transformers in the Real World: A Survey on NLP Applications
Inf., 2023 The field of Natural Language Processing (NLP) has undergone a significant transformation with the introduction of Transformers. From the first introduction of this technology in 2017, the use of transformers has become widespread and has had a profound ...
Narendra Patwardhan +2 more
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Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness? [PDF]
Annual Meeting of the Association for Computational Linguistics, 2020 With the growing popularity of deep-learning based NLP models, comes a need for interpretable systems. But what is interpretability, and what constitutes a high-quality interpretation?
Alon Jacovi, Yoav Goldberg
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Evaluation of ChatGPT for NLP-based Mental Health Applications [PDF]
arXiv.org, 2023 Large language models (LLM) have been successful in several natural language understanding tasks and could be relevant for natural language processing (NLP)-based mental health application research.
Bishal Lamichhane
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Classification of Nail Abnormalities using Convolutional Neural Network
วารสารวิทยาการสารสนเทศและเทคโนโลยีประยุกต์, 2023 Nails are one organ which can indicate the status of a health condition through its own appearance. To create a model which can be applied as a tool for self-classifying nail abnormalities, this article presents the study and analysis on seven ...
Nuttida Lapthanachai +3 more
doaj +1 more source
Post-hoc Interpretability for Neural NLP: A Survey [PDF]
ACM Computing Surveys, 2021 Neural networks for NLP are becoming increasingly complex and widespread, and there is a growing concern if these models are responsible to use. Explaining models helps to address the safety and ethical concerns and is essential for accountability ...
Andreas Madsen, Siva Reddy, A. Chandar
semanticscholar +1 more source
The effects of clinical hypnosis versus Neurolinguistic Programming (NLP) before External Cephalic Version (ECV) : a prospective off-centre randomised, double-blind, controlled trial [PDF]
, 2012 Objective. To examine the effects of clinical hypnosis versus NLP intervention on the success rate of ECV procedures in comparison to a control group. Methods.
Herrmann, Eva +5 more
core +2 more sources