Search Results for author:
Found 7 papers, 2 papers with code
Ignore Me But Don't Replace Me: Utilizing Non-Linguistic Elements for Pretraining on the Cybersecurity Domain
We use our domain-customized methodology to train CyBERTuned, a cybersecurity domain language model that outperforms other cybersecurity PLMs on most tasks.
WinoQueer: A Community-in-the-Loop Benchmark for Anti-LGBTQ+ Bias in Large Language Models
We present WinoQueer: a benchmark specifically designed to measure whether large language models (LLMs) encode biases that are harmful to the LGBTQ+ community.
DarkBERT: A Language Model for the Dark Side of the Internet
Recent research has suggested that there are clear differences in the language used in the Dark Web compared to that of the Surface Web.
Towards WinoQueer: Developing a Benchmark for Anti-Queer Bias in Large Language Models
This paper presents exploratory work on whether and to what extent biases against queer and trans people are encoded in large language models (LLMs) such as BERT.
Shedding New Light on the Language of the Dark Web
By leveraging CoDA, we conduct a thorough linguistic analysis of the Dark Web and examine the textual differences between the Dark Web and the Surface Web.
Generating Negative Samples by Manipulating Golden Responses for Unsupervised Learning of a Response Evaluation Model
Reference-based metrics that rely on comparisons to a set of known correct responses often fail to account for this variety, and consequently correlate poorly with human judgment.
