While recent advancements in large language models (LLMs) have enhanced their capabilities to solve mathematical problems, other aspects of numeracy remain underexplored. In this paper, we propose a benchmark to evaluate the ability of language models to perform basic numeracy tasks. We frame numeracy as a Natural Language Inference (NLI) task to assess the models’ ability to understand both numbers and language contexts. We evaluate 49 language models (LMs), including fine-tuned LMs on NLI datasets, instruction-tuned LLMs, and specialized math-LLMs. Our findings reveal three main insights: (1) LLMs only clearly outperform smaller LMs in arithmetic tasks, indicating that mathematical reasoning cannot be generalized to other numeracy skills such as number comparison and normalization; (2) while most language models achieve fair to good accuracy for NLI entailment cases, they still struggle to predict contradiction and neutral cases; and (3) the robustness of language models’ numeracy capabilities needs improvement, particularly in understanding the semantics and pragmatics of numbers in linguistic contexts.

In this short position paper, we highlight the importance of numbers in clinical text. We first present a taxonomy of number variants. We then perform corpus analysis to analyze characteristics of number use in several clinical corpora. Based on our findings of extensive use of numbers, and limited understanding of the impact of numbers on clinical NLP tasks, we identify the need for a public benchmark that will support investigation of numerical processing tasks for the clinical domain.

The development of text mining techniques for biomedical research literature has received increased attention in recent times. However, most of these techniques focus on prose, while much important biomedical data reside in tables. In this paper, we present a corpus created to serve as a gold standard for the development and evaluation of techniques for the automatic extraction of information from biomedical tables. We describe the guidelines used for corpus annotation and the manner in which they were developed. The high inter-annotator agreement achieved on the corpus, and the generic nature of our annotation approach, suggest that the developed guidelines can serve as a general framework for table annotation in biomedical and other scientific domains. The annotated corpus and the guidelines are available at http://www.csse.monash.edu.au/research/umnl/data/index.shtml.