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Found 9 papers, 7 papers with code
Deductive Closure Training of Language Models for Coherence, Accuracy, and Updatability
Given a collection of seed documents, DCT prompts LMs to generate additional text implied by these documents, reason globally about the correctness of this generated text, and finally fine-tune on text inferred to be correct.
DUnE: Dataset for Unified Editing
In this study, we broaden the scope of the editing problem to include an array of editing cases such as debiasing and rectifying reasoning errors and define an edit as any natural language expression that solicits a change in the model's outputs.
RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs
Despite their unprecedented success, even the largest language models make mistakes.
On Measuring Social Biases in Prompt-Based Multi-Task Learning
Large language models trained on a mixture of NLP tasks that are converted into a text-to-text format using prompts, can generalize into novel forms of language and handle novel tasks.
Challenges in Measuring Bias via Open-Ended Language Generation
Researchers have devised numerous ways to quantify social biases vested in pretrained language models.
Subspace Regularizers for Few-Shot Class Incremental Learning
The key to this approach is a new family of subspace regularization schemes that encourage weight vectors for new classes to lie close to the subspace spanned by the weights of existing classes.
Low-Resource Machine Translation Training Curriculum Fit for Low-Resource Languages
We conduct an empirical study of neural machine translation (NMT) for truly low-resource languages, and propose a training curriculum fit for cases when both parallel training data and compute resource are lacking, reflecting the reality of most of the world's languages and the researchers working on these languages.
Learning to Recombine and Resample Data for Compositional Generalization
Flexible neural sequence models outperform grammar- and automaton-based counterparts on a variety of tasks.
