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Found 7 papers, 2 papers with code
Don’t Search for a Search Method — Simple Heuristics Suffice for Adversarial Text Attacks
Recently more attention has been given to adversarial attacks on neural networks for natural language processing (NLP).
Post-edits Are Preferences Too
We attempt to use these implicit preferences for PO and show that it helps the model move towards post-edit-like hypotheses and away from machine translation-like hypotheses.
Prompting Large Language Models with Human Error Markings for Self-Correcting Machine Translation
While large language models (LLMs) pre-trained on massive amounts of unpaired language data have reached the state-of-the-art in machine translation (MT) of general domain texts, post-editing (PE) is still required to correct errors and to enhance term translation quality in specialized domains.
Enhancing Supervised Learning with Contrastive Markings in Neural Machine Translation Training
Supervised learning in Neural Machine Translation (NMT) typically follows a teacher forcing paradigm where reference tokens constitute the conditioning context in the model's prediction, instead of its own previous predictions.
Don't Search for a Search Method -- Simple Heuristics Suffice for Adversarial Text Attacks
Recently more attention has been given to adversarial attacks on neural networks for natural language processing (NLP).
Sparse Perturbations for Improved Convergence in Stochastic Zeroth-Order Optimization
Interest in stochastic zeroth-order (SZO) methods has recently been revived in black-box optimization scenarios such as adversarial black-box attacks to deep neural networks.
Correct Me If You Can: Learning from Error Corrections and Markings
Sequence-to-sequence learning involves a trade-off between signal strength and annotation cost of training data.
