We introduce Scientific Table Generation (STG), the task of extracting information from scientific documents to populate a table. We argue that STG can serve as a unifying task for structured knowledge extraction from scientific literature. To support this goal, we present the first multi-disciplinary benchmark for STG with predefined schemas and multi-record extraction, covering six scientific domains and comprising 20 datasets unified under our proposed formulation. We detail the benchmark’s construction, including dataset composition, and introduce an STG evaluation protocol. We analyse the results of zero-shot large language model (LLM) and vision-language model (VLM) pipelines with typical document processing AI methods for the STG task. In doing so, we identify key challenges of the STG task and propose a set of open challenges, including multimodality, table and figure understanding, normalisation of data, and treatment of reported quantities. All code and data accompanying this work are publicly available at[This repository will save researchers significant time. Instead of manually searching for 20 different datasets, researchers can use our repository to download all datasets directly, as a download link is provided for each paper. In addition, researchers can use our evaluation framework, which streamlines the process and improves overall efficiency.]: https://github.com/bolucunecva/table_generation

The rapid growth of scientific literature has made manual extraction of structured knowledge increasingly impractical. To address this challenge, we introduce SCILIRE, a system for creating datasets from scientific literature. SCILIRE has been designed around Human-AI teaming principles centred on workflows for verifying and curating data. It facilitates an iterative workflow in which researchers can review and correct AI outputs. Furthermore, this interaction is used as a feedback signal to improve future LLM-based inference. We evaluate our design using a combination of intrinsic benchmarking outcomes together with real-world case studies across multiple domains. The results demonstrate that SCILIRE improves extraction fidelity and facilitates efficient dataset creation.