Search Results for author:
Found 8 papers, 4 papers with code
LUSIFER: Language Universal Space Integration for Enhanced Multilingual Embeddings with Large Language Models
Extensive experimental results demonstrate that LUSIFER significantly enhances the multilingual performance across various embedding tasks, particularly for medium and low-resource languages, without requiring explicit multilingual training data.
Comprehensive and Practical Evaluation of Retrieval-Augmented Generation Systems for Medical Question Answering
Retrieval-augmented generation (RAG) has emerged as a promising approach to enhance the performance of large language models (LLMs) in knowledge-intensive tasks such as those from medical domain.
Zero-shot Cross-lingual Transfer Learning with Multiple Source and Target Languages for Information Extraction: Language Selection and Adversarial Training
The majority of previous researches addressing multi-lingual IE are limited to zero-shot cross-lingual single-transfer (one-to-one) setting, with high-resource languages predominantly as source training data.
ULLME: A Unified Framework for Large Language Model Embeddings with Generation-Augmented Learning
This is due to their causal attention mechanism and the misalignment between their pre-training objectives and the text ranking tasks.
CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages
However, when it comes to training datasets for these LLMs, especially the recent state-of-the-art models, they are often not fully disclosed.
Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback
Okapi introduces instruction and response-ranked data in 26 diverse languages to facilitate the experiments and development of future multilingual LLM research.
ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning
The answer to this question requires a thorough evaluation of ChatGPT over multiple tasks with diverse languages and large datasets (i. e., beyond reported anecdotes), which is still missing or limited in current research.
FAMIE: A Fast Active Learning Framework for Multilingual Information Extraction
FAMIE is designed to address a fundamental problem in existing AL frameworks where annotators need to wait for a long time between annotation batches due to the time-consuming nature of model training and data selection at each AL iteration.
