Table pre-training: A survey on model architectures, pre-training objectives, and downstream tasks
Since a vast number of tables can be easily collected from web pages, spreadsheets, PDFs,
and various other document types, a flurry of table pre-training frameworks have been …
and various other document types, a flurry of table pre-training frameworks have been …
MultiHiertt: Numerical reasoning over multi hierarchical tabular and textual data
Numerical reasoning over hybrid data containing both textual and tabular content (eg,
financial reports) has recently attracted much attention in the NLP community. However …
financial reports) has recently attracted much attention in the NLP community. However …
A survey on llm-gernerated text detection: Necessity, methods, and future directions
The powerful ability to understand, follow, and generate complex language emerging from
large language models (LLMs) makes LLM-generated text flood many areas of our daily …
large language models (LLMs) makes LLM-generated text flood many areas of our daily …
Large language models on tabular data--a survey
Recent breakthroughs in large language modeling have facilitated rigorous exploration of
their application in diverse tasks related to tabular data modeling, such as prediction, tabular …
their application in diverse tasks related to tabular data modeling, such as prediction, tabular …
Hitab: A hierarchical table dataset for question answering and natural language generation
Tables are often created with hierarchies, but existing works on table reasoning mainly focus
on flat tables and neglect hierarchical tables. Hierarchical tables challenge existing methods …
on flat tables and neglect hierarchical tables. Hierarchical tables challenge existing methods …
Towards explainable evaluation metrics for machine translation
Unlike classical lexical overlap metrics such as BLEU, most current evaluation metrics for
machine translation (for example, COMET or BERTScore) are based on black-box large …
machine translation (for example, COMET or BERTScore) are based on black-box large …
Coherence boosting: When your pretrained language model is not paying enough attention
Long-range semantic coherence remains a challenge in automatic language generation
and understanding. We demonstrate that large language models have insufficiently learned …
and understanding. We demonstrate that large language models have insufficiently learned …
Transformers go for the LOLs: Generating (humourous) titles from scientific abstracts end-to-end
We consider the end-to-end abstract-to-title generation problem, exploring seven recent
transformer based models (including ChatGPT) fine-tuned on more than 30k abstract-title …
transformer based models (including ChatGPT) fine-tuned on more than 30k abstract-title …
Effective distillation of table-based reasoning ability from llms
Large Language Models (LLMs) have demonstrated remarkable performance across a wide
range of natural language processing tasks. However, their remarkable parameter size and …
range of natural language processing tasks. However, their remarkable parameter size and …
Scitab: A challenging benchmark for compositional reasoning and claim verification on scientific tables
Current scientific fact-checking benchmarks exhibit several shortcomings, such as biases
arising from crowd-sourced claims and an over-reliance on text-based evidence. We present …
arising from crowd-sourced claims and an over-reliance on text-based evidence. We present …