🔗 Who cites «From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models»

49 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · ← back to «From Decoding to Meta-Generation: Inference-time Algorithms for Large Language Models»

1. s1: Simple test-time scalingConference on Empirical Methods in Natural Language Processing (EMNLP)Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.2025 · 608 citations
2. 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
3. L1: Controlling How Long A Reasoning Model Thinks With Reinforcement LearningarXiv:2503.04697Pranjal Aggarwal, Sean Welleck2025 · 125 citations
4. Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language ModelsarXiv:2408.00724Yangzhen Wu, Zhiqing Sun, Shanda Li, et al.2024 · 122 citations
5. A Thorough Examination of Decoding Methods in the Era of LLMsConference on Empirical Methods in Natural Language Processing (EMNLP)Chufan Shi, Haoran Yang, Deng Cai, et al.2024 · 68 citations
6. PlanGEN: A Multi-Agent Framework for Generating Planning and Reasoning Trajectories for Complex Problem SolvingConference on Empirical Methods in Natural Language Processing (EMNLP)Mihir Parmar, Xin Liu, Palash Goyal, et al.2025 · 16 citations
7. Examining False Positives under Inference Scaling for Mathematical ReasoningConference on Empirical Methods in Natural Language Processing (EMNLP)Yu Wang, Nan Yang, Liang Wang, et al.2025 · 9 citations
8. GCoT-Decoding: Unlocking Deep Reasoning Paths for Universal Question AnsweringFindings of the Association for Computational LinguisticsGuanran Luo, Wentao Qiu, Zhongquan Jian, et al.2026 · 6 citations
9. G2: Guided Generation for Enhanced Output Diversity in LLMsConference on Empirical Methods in Natural Language Processing (EMNLP)Zhiwen Ruan, Yixia Li, Yefeng Liu, et al.2025 · 6 citations
10. When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMsConference on Empirical Methods in Natural Language Processing (EMNLP)Ammar Khairi, Daniel D'souza, Ye Shen, et al.2025 · 6 citations
11. Sense and Sensitivity: “Reasoning” Models are More Robust, but can Diverge from Human Consensus in a Legal Interpretation TaskConference on Computational Natural Language Learning (CoNLL)Dawson Petersen, Abhishek Purushothama, Nathan Schneider2026 · 1 citation
12. 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
13. GIFT: Guided Fine-Tuning and Transfer for Enhancing Instruction-Tuned Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhiwen Ruan, Yichao Du, Jianjie Zheng, et al.2026

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

This page in Markdown → https://md.research.elamur.ai/free/en/papers/welleck-2024-decoding-meta-generation-inference-time/cited-by/