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 …

Table reasoning aims to generate inference results based on the user requirement and the
provided table. Enhancing the table reasoning capability of the model can aid in obtaining …

The dominant paradigm for neural text generation is left-to-right decoding from
autoregressive language models. Constrained or controllable generation under complex …

Large language models (LLMs) have achieved remarkable performance on a variety of
natural language understanding tasks. However, existing benchmarks are inadequate in …

We present ToTTo, an open-domain English table-to-text dataset with over 120,000 training
examples that proposes a controlled generation task: given a Wikipedia table and a set of …

Recent literature has shown that large language models (LLMs) are generally excellent few-
shot reasoners to solve text reasoning tasks. However, the capability of LLMs on table …

Charts are commonly used for exploring data and communicating insights. Generating
natural language summaries from charts can be very helpful for people in inferring key …

We present DART, an open domain structured DAta Record to Text generation dataset with
over 82k instances (DARTs). Data-to-Text annotations can be a costly process, especially …

Data-to-text generation has recently attracted substantial interests due to its wide
applications. Existing methods have shown impressive performance on an array of tasks …

Abstract The task of Question Answering (QA) has attracted significant research interest for a
long time. Its relevance to language understanding and knowledge retrieval tasks, along …