반응형 오늘의 자연어 처리572 [2023-09-10] 오늘의 자연어처리 XGen-7B Technical Report Large Language Models (LLMs) have become ubiquitous across various domains, transforming the way we interact with information and conduct research. However, most high-performing LLMs remain confined behind proprietary walls, hindering scientific progress. Most open-source LLMs, on the other hand, are limited in their ability to support longer sequence lengths, which is a.. 2023. 9. 10. [2023-09-09] 오늘의 자연어처리 Uncovering Drift in Textual Data: An Unsupervised Method for Detecting and Mitigating Drift in Machine Learning Models Drift in machine learning refers to the phenomenon where the statistical properties of data or context, in which the model operates, change over time leading to a decrease in its performance. Therefore, maintaining a constant monitoring process for machine learning model perform.. 2023. 9. 9. [2023-09-08] 오늘의 자연어처리 Offensive Hebrew Corpus and Detection using BERT Offensive language detection has been well studied in many languages, but it is lagging behind in low-resource languages, such as Hebrew. In this paper, we present a new offensive language corpus in Hebrew. A total of 15,881 tweets were retrieved from Twitter. Each was labeled with one or more of five classes (abusive, hate, violence, pornographic.. 2023. 9. 8. [2023-09-07] 오늘의 자연어처리 On the Challenges of Building Datasets for Hate Speech Detection Detection of hate speech has been formulated as a standalone application of NLP and different approaches have been adopted for identifying the target groups, obtaining raw data, defining the labeling process, choosing the detection algorithm, and evaluating the performance in the desired setting. However, unlike other downstream ta.. 2023. 9. 7. 이전 1 ··· 28 29 30 31 32 33 34 ··· 143 다음 반응형