67 citations · 73 across the 8 of their papers we have counts for
8 papers
Is Bigger and Deeper Always Better? Probing LLaMA Across Scales and Layers
Nuo Chen, Ning Wu, Shining Liang +4
This paper presents an in-depth analysis of Large Language Models (LLMs), focusing on LLaMA, a prominent open-source foundational model in natural language processing. Instead of a…
Breaking Language Barriers in Multilingual Mathematical Reasoning: Insights and Observations
Nuo Chen, Zinan Zheng, Ning Wu +3
Existing research predominantly focuses on developing powerful language learning models (LLMs) for mathematical reasoning within monolingual languages, with few explorations in pre…
RUEL: Retrieval-Augmented User Representation with Edge Browser Logs for Sequential Recommendation
Ning Wu, Ming Gong, Linjun Shou +2
Online recommender systems (RS) aim to match user needs with the vast amount of resources available on various platforms. A key challenge is to model user preferences accurately un…
Empowering Dual-Encoder with Query Generator for Cross-Lingual Dense Retrieval
Houxing Ren, Linjun Shou, Ning Wu +2
In monolingual dense retrieval, lots of works focus on how to distill knowledge from cross-encoder re-ranker to dual-encoder retriever and these methods achieve better performance…
Lexicon-Enhanced Self-Supervised Training for Multilingual Dense Retrieval
Houxing Ren, Linjun Shou, Jian Pei +3
Recent multilingual pre-trained models have shown better performance in various multilingual tasks. However, these models perform poorly on multilingual retrieval tasks due to lack…
Large Language Models are Diverse Role-Players for Summarization Evaluation
Ning Wu, Ming Gong, Linjun Shou +2
Text summarization has a wide range of applications in many scenarios. The evaluation of the quality of the generated text is a complex problem. A big challenge to language evaluat…
Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense Retrieval
Ning Wu, Yaobo Liang, Houxing Ren +4
Recent research demonstrates the effectiveness of using pretrained language models (PLM) to improve dense retrieval and multilingual dense retrieval. In this work, we present a sim…
XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation
Yaobo Liang, Nan Duan, Yeyun Gong +21
In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evalu…