Math Natural Language Inference: this should be easy!
This addresses the challenge of evaluating LLMs' reasoning abilities in mathematical contexts for AI and NLP researchers, though it is incremental as it builds on existing NLI frameworks.
The study tackled the problem of whether contemporary LLMs can perform natural language inference on mathematical texts, finding that while a majority vote of LLMs approximates human-labeled data in some settings, they still struggle with basic inferences in mathematical language.
We ask whether contemporary LLMs are able to perform natural language inference (NLI) tasks on mathematical texts. We call this the Math NLI problem. We construct a corpus of Math NLI pairs whose premises are from extant mathematical text and whose hypotheses and gold labels were provided by people with experience in both research-level mathematics and also in the NLI field. We also investigate the quality of corpora using the same premises but whose hypotheses are provided by LLMs themselves. We not only investigate the performance but also the inter-group consistency of the diverse group of LLMs. We have both positive and negative findings. Among our positive findings: in some settings, using a majority vote of LLMs is approximately equivalent to using human-labeled data in the Math NLI area. On the negative side: LLMs still struggle with mathematical language. They occasionally fail at even basic inferences. Current models are not as prone to hypothesis-only "inference" in our data the way the previous generation had been. In addition to our findings, we also provide our corpora as data to support future work on Math NLI.