Search Results for author:
Found 6 papers, 5 papers with code
Rethinking Negative Instances for Generative Named Entity Recognition
In the Named Entity Recognition (NER) task, recent advancements have seen the remarkable improvement of LLMs in a broad range of entity domains via instruction tuning, by adopting entity-centric schema.
Mathematical Language Models: A Survey
In recent years, there has been remarkable progress in leveraging Language Models (LMs), encompassing Pre-trained Language Models (PLMs) and Large-scale Language Models (LLMs), within the domain of mathematics.
OpenBA: An Open-sourced 15B Bilingual Asymmetric seq2seq Model Pre-trained from Scratch
This report provides the main details to pre-train an analogous model, including pre-training data processing, Bilingual Flan data collection, the empirical observations that inspire our model architecture design, training objectives of different stages, and other enhancement techniques.
CMD: a framework for Context-aware Model self-Detoxification
In view of this, we introduce a Context-aware Model self-Detoxification~(CMD) framework that pays attention to both the context and the detoxification process, i. e., first detoxifying the context and then making the language model generate along the safe context.
Robust Question Answering against Distribution Shifts with Test-Time Adaptation: An Empirical Study
To answer this question, we evaluate test-time adaptation (TTA) to improve a model after deployment.
SelfMix: Robust Learning Against Textual Label Noise with Self-Mixup Training
The conventional success of textual classification relies on annotated data, and the new paradigm of pre-trained language models (PLMs) still requires a few labeled data for downstream tasks.
