14 citations · 49 across the 14 of their papers we have counts for
14 papers
M-Longdoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework
Yew Ken Chia, Liying Cheng, Hou Pong Chan +5
The ability to understand and answer questions over documents can be useful in many business and practical applications. However, documents often contain lengthy and diverse multim…
SeaLLMs -- Large Language Models for Southeast Asia
Xuan-Phi Nguyen, Wenxuan Zhang, Xin Li +14
Despite the remarkable achievements of large language models (LLMs) in various tasks, there remains a linguistic bias that favors high-resource languages, such as English, often at…
Exploring the Potential of Large Language Models in Computational Argumentation
Guizhen Chen, Liying Cheng, Luu Anh Tuan +1
Computational argumentation has become an essential tool in various domains, including law, public policy, and artificial intelligence. It is an emerging research field in natural…
Large Language Models can Contrastively Refine their Generation for Better Sentence Representation Learning
Huiming Wang, Zhaodonghui Li, Liying Cheng +2
Recently, large language models (LLMs) have emerged as a groundbreaking technology and their unparalleled text generation capabilities have sparked interest in their application to…
AQE: Argument Quadruplet Extraction via a Quad-Tagging Augmented Generative Approach
Jia Guo, Liying Cheng, Wenxuan Zhang +3
Argument mining involves multiple sub-tasks that automatically identify argumentative elements, such as claim detection, evidence extraction, stance classification, etc. However, e…
Enhancing Few-shot NER with Prompt Ordering based Data Augmentation
Huiming Wang, Liying Cheng, Wenxuan Zhang +2
Recently, data augmentation (DA) methods have been proven to be effective for pre-trained language models (PLMs) in low-resource settings, including few-shot named entity recogniti…
Is GPT-4 a Good Data Analyst?
Liying Cheng, Xingxuan Li, Lidong Bing
As large language models (LLMs) have demonstrated their powerful capabilities in plenty of domains and tasks, including context understanding, code generation, language generation,…
Unlocking Temporal Question Answering for Large Language Models with Tailor-Made Reasoning Logic
Xingxuan Li, Liying Cheng, Qingyu Tan +3
The temporal aspect is a significant dimension of our reality. We notice the challenge that large language models (LLMs) face when engaging in temporal reasoning. Our preliminary e…
Large Language Models are Not Yet Human-Level Evaluators for Abstractive Summarization
Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen +2
With the recent undeniable advancement in reasoning abilities in large language models (LLMs) like ChatGPT and GPT-4, there is a growing trend for using LLMs on various tasks. One…
A Hierarchical Encoding-Decoding Scheme for Abstractive Multi-document Summarization
Chenhui Shen, Liying Cheng, Xuan-Phi Nguyen +2
Pre-trained language models (PLMs) have achieved outstanding achievements in abstractive single-document summarization (SDS). However, such benefits may not fully extend to multi-d…
SentBS: Sentence-level Beam Search for Controllable Summarization
Chenhui Shen, Liying Cheng, Lidong Bing +2
A wide range of control perspectives have been explored in controllable text generation. Structure-controlled summarization is recently proposed as a useful and interesting researc…
IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks
Liying Cheng, Lidong Bing, Ruidan He +3
Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking th…
MReD: A Meta-Review Dataset for Structure-Controllable Text Generation
Chenhui Shen, Liying Cheng, Ran Zhou +3
When directly using existing text generation datasets for controllable generation, we are facing the problem of not having the domain knowledge and thus the aspects that could be c…
On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation
Ruidan He, Linlin Liu, Hai Ye +6
Adapter-based tuning has recently arisen as an alternative to fine-tuning. It works by adding light-weight adapter modules to a pretrained language model (PrLM) and only updating t…