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Creators/Authors contains: "Thakur, Amitayush"

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  1. ; ; ; ; (, Conference on Language Models (CoLM 2024))
    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ; ; (, Neural Information Processing Systems (NeurIPS), 2024)
    We present PUTNAMBENCH, a new multilingual benchmark for evaluating the ability of neural theorem-provers to solve competition mathematics problems. PUTNAMBENCH consists of 1697 hand-constructed formalizations of 640 theorems sourced from the William Lowell Putnam Mathematical Competition, the premier undergraduate-level mathematics competition in North America. All the theorems have formalizations in Lean 4 and Isabelle; a substantial subset also has Coq formalizations. Proving the theorems requires significant problem-solving ability and proficiency in a broad range of topics taught in undergraduate mathematics courses. We use PUTNAMBENCH to evaluate several established neural and symbolic theorem-provers. These approaches can only solve a handful of the PUTNAMBENCH problems, establishing the benchmark as a difficult open challenge for research on neural theorem-proving. PUTNAMBENCH is available at https://github.com/trishullab/PutnamBench. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ; (, Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks track)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; ; ; ; (, Neural Information Processing Systems)
    We present PutnamBench, a new multi-language benchmark for evaluating the ability of neural theorem-provers to solve competition mathematics problems. PutnamBench consists of 1692 hand-constructed formalizations of 640 theorems sourced from the William Lowell Putnam Mathematical Competition, the premier undergraduate-level mathematics competition in North America. All the problems have formalizations in Lean 4 and Isabelle; a substantial subset also has Coq formalizations. PutnamBench requires significant problem-solving ability and proficiency in a broad range of topics taught in undergraduate mathematics courses. We use PutnamBench to evaluate several established neural and symbolic theorem-provers. These approaches can only solve a handful of the PutnamBench problems, establishing the benchmark as a difficult open challenge for research on neural theorem-proving. PutnamBench is available at https://github.com/trishullab/PutnamBench. 
    more » « less
    Accepted Manuscript Full Text Available
  5. ; ; ; ; (, Conference on Language Models, 2024)
    Accepted Manuscript Full Text Available