13 citations · 23 across the 6 of their papers we have counts for
6 papers
An Empirical Study of Many-to-Many Summarization with Large Language Models
Jiaan Wang, Fandong Meng, Zengkui Sun +5
Many-to-many summarization (M2MS) aims to process documents in any language and generate the corresponding summaries also in any language. Recently, large language models (LLMs) ha…
CT-Eval: Benchmarking Chinese Text-to-Table Performance in Large Language Models
Haoxiang Shi, Jiaan Wang, Jiarong Xu +2
Text-to-Table aims to generate structured tables to convey the key information from unstructured documents. Existing text-to-table datasets are typically oriented English, limiting…
Towards Consistency Filtering-Free Unsupervised Learning for Dense Retrieval
Haoxiang Shi, Sumio Fujita, Tetsuya Sakai
Domain transfer is a prevalent challenge in modern neural Information Retrieval (IR). To overcome this problem, previous research has utilized domain-specific manual annotations an…
Is ChatGPT a Good NLG Evaluator? A Preliminary Study
Jiaan Wang, Yunlong Liang, Fandong Meng +6
Recently, the emergence of ChatGPT has attracted wide attention from the computational linguistics community. Many prior studies have shown that ChatGPT achieves remarkable perform…
GOAL: Towards Benchmarking Few-Shot Sports Game Summarization
Jiaan Wang, Tingyi Zhang, Haoxiang Shi
Sports game summarization aims to generate sports news based on real-time commentaries. The task has attracted wide research attention but is still under-explored probably due to t…
Self-supervised Document Clustering Based on BERT with Data Augment
Haoxiang Shi, Cen Wang
Contrastive learning is a promising approach to unsupervised learning, as it inherits the advantages of well-studied deep models without a dedicated and complex model design. In th…