30 citations · 101 across the 45 of their papers we have counts for
6 papers · 2 filters
A Survey of Conversational Search
Fengran Mo, Kelong Mao, Ziliang Zhao +7
As a cornerstone of modern information access, search engines have become indispensable in everyday life. With the rapid advancements in AI and natural language processing (NLP) te…
Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation
Qiwei Shao, Fengran Mo, Jian-Yun Nie
Document-level biomedical concept extraction is the task of identifying biomedical concepts mentioned in a given document. Recent advancements have adapted pre-trained language mod…
DoRA: Enhancing Parameter-Efficient Fine-Tuning with Dynamic Rank Distribution
Yulong Mao, Kaiyu Huang, Changhao Guan +3
Fine-tuning large-scale pre-trained models is inherently a resource-intensive task. While it can enhance the capabilities of the model, it also incurs substantial computational cos…
Language Modeling Using Tensor Trains
Zhan Su, Yuqin Zhou, Fengran Mo +1
We propose a novel tensor network language model based on the simplest tensor network (i.e., tensor trains), called `Tensor Train Language Model' (TTLM). TTLM represents sentences…
A Survey on Large Language Models with Multilingualism: Recent Advances and New Frontiers
Kaiyu Huang, Fengran Mo, Xinyu Zhang +10
The rapid development of Large Language Models (LLMs) demonstrates remarkable multilingual capabilities in natural language processing, attracting global attention in both academia…
A User-Centric Multi-Intent Benchmark for Evaluating Large Language Models
Jiayin Wang, Fengran Mo, Weizhi Ma +3
Large language models (LLMs) are essential tools that users employ across various scenarios, so evaluating their performance and guiding users in selecting the suitable service is…