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Found 7 papers, 1 papers with code
Against The Achilles' Heel: A Survey on Red Teaming for Generative Models
Generative models are rapidly gaining popularity and being integrated into everyday applications, raising concerns over their safety issues as various vulnerabilities are exposed.
A Chinese Dataset for Evaluating the Safeguards in Large Language Models
Previous studies have proposed comprehensive taxonomies of the risks posed by LLMs, as well as corresponding prompts that can be used to examine the safety mechanisms of LLMs.
COVID-SEE: Scientific Evidence Explorer for COVID-19 Related Research
We present COVID-SEE, a system for medical literature discovery based on the concept of information exploration, which builds on several distinct text analysis and natural language processing methods to structure and organise information in publications, and augments search by providing a visual overview supporting exploration of a collection to identify key articles of interest.
Improving Chemical Named Entity Recognition in Patents with Contextualized Word Embeddings
In this paper, we explore the NER performance of a BiLSTM-CRF model utilising pre-trained word embeddings, character-level word representations and contextualized ELMo word representations for chemical patents.
A bag-of-concepts model improves relation extraction in a narrow knowledge domain with limited data
This paper focuses on a traditional relation extraction task in the context of limited annotated data and a narrow knowledge domain.
Detecting Chemical Reactions in Patents
Extracting chemical reactions from patents is a crucial task for chemists working on chemical exploration.
Comparing CNN and LSTM character-level embeddings in BiLSTM-CRF models for chemical and disease named entity recognition
We compare the use of LSTM-based and CNN-based character-level word embeddings in BiLSTM-CRF models to approach chemical and disease named entity recognition (NER) tasks.
