REVIEW 2 cited by
Natural Language Premise Selection: Finding Supporting Statements for Mathematical Text
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Mathematical text is written using a combination of words and mathematical expressions. This combination, along with a specific way of structuring sentences makes it challenging for state-of-art NLP tools to understand and reason on top of mathematical discourse. In this work, we propose a new NLP task, the natural premise selection, which is used to retrieve supporting definitions and supporting propositions that are useful for generating an informal mathematical proof for a particular statement. We also make available a dataset, NL-PS, which can be used to evaluate different approaches for the natural premise selection task. Using different baselines, we demonstrate the underlying interpretation challenges associated with the task.
Forward citations
Cited by 2 Pith papers
-
From Informal to Formal -- Incorporating and Evaluating LLMs on Natural Language Requirements to Verifiable Formal Proofs
The authors create an 18,000-pair formal-verification dataset, evaluate ten LLMs on six subtasks in five languages, and find that 7-8B fine-tuned models match DeepSeek-R1-671B while also yielding mixed but positive tr...
-
Hierarchical Attention Generates Better Proofs
A hierarchical attention regularizer improves pass@64 on Lean theorem proving benchmarks by about two percentage points, while its proof-complexity reduction is computed on a small subset and is less robust.
Discussion (0). Continue with ORCID to comment.