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Found 7 papers, 0 papers with code
JaColBERT and Hard Negatives, Towards Better Japanese-First Embeddings for Retrieval: Early Technical Report
Document retrieval in many languages has been largely relying on multi-lingual models, and leveraging the vast wealth of English training data.
Large Language Models as Batteries-Included Zero-Shot ESCO Skills Matchers
We generate synthetic training data for the entirety of ESCO skills and train a classifier to extract skill mentions from job posts.
Large Language Models in the Workplace: A Case Study on Prompt Engineering for Job Type Classification
Furthermore, we observe that the wording of the prompt is a critical factor in eliciting the appropriate "reasoning" in the model, and that seemingly minor aspects of the prompt significantly affect the model's performance.
The Unreasonable Effectiveness of the Baseline: Discussing SVMs in Legal Text Classification
We aim to highlight an interesting trend to contribute to the ongoing debate around advances within legal Natural Language Processing.
Ranked #8 on
Natural Language Understanding
on LexGLUE
LegaLMFiT: Efficient Short Legal Text Classification with LSTM Language Model Pre-Training
Large Transformer-based language models such as BERT have led to broad performance improvements on many NLP tasks.
Deep Embeddings of Contextual Assessment Data for Improving Performance Prediction
We introduce DeepPerfEmb, or DPE, a new deep-learning model that captures dense representations of students’ online behaviour and meta-data about students and educational content.
EduBERT: Pretrained Deep Language Models for Learning Analytics
The use of large pretrained neural networks to create contextualized word embeddings has drastically improved performance on several natural language processing (NLP) tasks.
