2 citations · 5 across the 9 of their papers we have counts for
9 papers
HiRAS: A Hierarchical Multi-Agent Framework for Paper-to-Code Generation and Execution
Hanhua Hong, Yizhi LI, Jiaoyan Chen +4
Recent advances in large language models have highlighted their potential to automate computational research, particularly reproducing experimental results. However, existing appro…
Ara-HOPE: Human-Centric Post-Editing Evaluation for Dialectal Arabic to Modern Standard Arabic Translation
Abdullah Alabdullah, Lifeng Han, Chenghua Lin
Dialectal Arabic to Modern Standard Arabic (DA-MSA) translation is a challenging task in Machine Translation (MT) due to significant lexical, syntactic, and semantic divergences be…
FineWeb-zhtw: Scalable Curation of Traditional Chinese Text Data from the Web
Cheng-Wei Lin, Wan-Hsuan Hsieh, Kai-Xin Guan +6
The quality and size of a pretraining dataset significantly influence the performance of large language models (LLMs). While there have been numerous efforts in the curation of suc…
On the Rigour of Scientific Writing: Criteria, Analysis, and Insights
Joseph James, Chenghao Xiao, Yucheng Li +1
Rigour is crucial for scientific research as it ensures the reproducibility and validity of results and findings. Despite its importance, little work exists on modelling rigour com…
With Ears to See and Eyes to Hear: Sound Symbolism Experiments with Multimodal Large Language Models
Tyler Loakman, Yucheng Li, Chenghua Lin
Recently, Large Language Models (LLMs) and Vision Language Models (VLMs) have demonstrated aptitude as potential substitutes for human participants in experiments testing psycholin…
Evaluating Large Language Models for Generalization and Robustness via Data Compression
Yucheng Li, Yunhao Guo, Frank Guerin +1
Existing methods for evaluating large language models face challenges such as data contamination, sensitivity to prompts, and the high cost of benchmark creation. To address this,…
Finding Challenging Metaphors that Confuse Pretrained Language Models
Yucheng Li, Frank Guerin, Chenghua Lin
Metaphors are considered to pose challenges for a wide spectrum of NLP tasks. This gives rise to the area of computational metaphor processing. However, it remains unclear what typ…
Language Model as an Annotator: Unsupervised Context-aware Quality Phrase Generation
Zhihao Zhang, Yuan Zuo, Chenghua Lin +1
Phrase mining is a fundamental text mining task that aims to identify quality phrases from context. Nevertheless, the scarcity of extensive gold labels datasets, demanding substant…
LatestEval: Addressing Data Contamination in Language Model Evaluation through Dynamic and Time-Sensitive Test Construction
Yucheng Li, Frank Guerin, Chenghua Lin
Data contamination in evaluation is getting increasingly prevalent with the emergence of language models pre-trained on super large, automatically crawled corpora. This problem lea…