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Who cites «Don't Trust: Verify — Grounding LLM Quantitative Reasoning with Autoformalization»

42 papers of our library cite it (of the 149,439 whose bibliographies we read) · 20 with a page here, the most cited first · ← back to «Don't Trust: Verify — Grounding LLM Quantitative Reasoning with Autoformalization»

  1. Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language ModelsScience China Information SciencesQiguang Chen, Libo Qin, Jinhao Liu, et al.2025 · 176 citations
  2. Lean-STaR: Learning to Interleave Thinking and ProvingarXiv:2407.10040Haohan Lin, Zhiqing Sun, Sean Welleck, et al.2024 · 51 citations
  3. Formal Mathematical Reasoning: A New Frontier in AIarXiv:2412.16075Kaiyu Yang, Gabriel Poesia, Jingxuan He, et al.2024 · 49 citations
  4. A Survey on Deep Learning for Theorem ProvingarXiv:2404.09939Zhaoyu Li, Jialiang Sun, Logan Murphy, et al.2024 · 46 citations
  5. Herald: A Natural Language Annotated Lean 4 DatasetarXiv:2410.10878Guoxiong Gao, Yutong Wang, Jiedong Jiang, et al.2024 · 33 citations
  6. Autoformalize Mathematical Statements by Symbolic Equivalence and Semantic ConsistencyConference on Neural Information Processing Systems (NeurIPS)Zenan Li, Yifan Wu, Zhaoyu Li, et al.2024 · 19 citations
  7. Safe: Enhancing Mathematical Reasoning in Large Language Models via Retrospective Step-aware Formal VerificationAnnual Meeting of the Association for Computational Linguistics (ACL)Chengwu Liu, Ye Yuan, Yichun Yin, et al.2025 · 17 citations
  8. ATLAS: Autoformalizing Theorems through Lifting, Augmentation, and Synthesis of DataConference on Neural Information Processing Systems (NeurIPS)Xiaoyang Liu, Kangjie Bao, Jiashuo Zhang, et al.2025 · 16 citations
  9. Neuro-Symbolic Data Generation for Math ReasoningConference on Neural Information Processing Systems (NeurIPS)Zenan Li, Zhi Zhou, Yuan Yao, et al.2024 · 14 citations
  10. Theorem Prover as a Judge for Synthetic Data GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Joshua Ong Jun Leang, Giwon Hong, Wenda Li, et al.2025 · 9 citations
  11. A Survey on Feedback-based Multi-step Reasoning for Large Language Models on MathematicsarXiv:2502.14333Ting-Ruen Wei, Haowei Liu, Xuyang Wu, et al.2025 · 7 citations
  12. Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math CapabilityConference on Empirical Methods in Natural Language Processing (EMNLP)Ruida Wang, Yuxin Li, Yi R. Fung, et al.2025 · 7 citations
  13. StepFun-Formalizer: Unlocking the Autoformalization Potential of LLMs through Knowledge-Reasoning FusionarXiv:2508.04440Yutong Wu, Di Huang, Ruosi Wan, et al.2025 · 7 citations
  14. ProofFlow: A Dependency Graph Approach to Faithful Proof AutoformalizationarXiv:2510.15981Rafael Cabral, Tuan Manh Do, Xuejun Yu, et al.2025 · 5 citations
  15. Graders Should Cheat: Privileged Information Enables Expert-Level Automated EvaluationsConference on Empirical Methods in Natural Language Processing (EMNLP)Jin Peng Zhou, Séb Arnold, Nan Ding, et al.2025 · 3 citations
  16. Argument-Based Consistency in Toxicity Explanations of LLMsFindings of the Association for Computational LinguisticsRamaravind Kommiya Mothilal, Joanna Roy, Syed Ishtiaque Ahmed, et al.2026 · 2 citations
  17. ReProbe: Efficient Test-Time Scaling of Multi-Step Reasoning by Probing Internal States of Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Jingwei Ni, Ekaterina Fadeeva, Tianyi Wu, et al.2026 · 1 citation
  18. Solve-Detect-Verify: Inference-Time Scaling with Flexible Generative VerifierAnnual Meeting of the Association for Computational Linguistics (ACL)Jianyuan Zhong, Zeju Li, Zhijian Xu, et al.2026 · 1 citation
  19. Format-Adapter: Improving Reasoning Capability of LLMs by Adapting Suitable FormatFindings of the Association for Computational LinguisticsDingzirui Wang, Xuanliang Zhang, Rongyu Cao, et al.2026
  20. STCOR: A Trilevel Syllogism-Driven Reasoning FrameworkWorkshop on Argument MiningKeying Yang, Hao Wang, Chengtao Jian, et al.2026

22 more papers of our library cite it; they have no page here yet.

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