Universal Self-Consistency for Large Language Model Generation (2023 · 92 citations)
Standard self-consistency samples several chain-of-thought solutions and takes a majority vote over the final answers.
Natural Language to Code Generation in Interactive Data Science Notebooks (2023 · 63 citations)
Data scientists often work in Jupyter-style notebooks, where each new request to an AI coding assistant depends on earlier cells, their execution state, and previous requests.

All works

4 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

Efficiently Scaling Transformer Inference ↗arXiv:2211.05102 · no summary yetReiner Pope et al.2022 · 368 citations
Universal Self-Consistency for Large Language Model GenerationarXiv:2311.17311Xinyun Chen, Renat Aksitov, Uri Alon, et al.2023 · 92 citations
Natural Language to Code Generation in Interactive Data Science NotebooksAnnual Meeting of the Association for Computational Linguistics (ACL)Pengcheng Yin, Wen-Ding Li, Kefan Xiao, et al.2023 · 63 citations
Measuring The Impact Of Programming Language Distribution ↗arXiv:2302.01973 · no summary yetGabriel Orlanski et al.2023 · 22 citations

Co-authors here

12 of 14 co-authors with a page, the most papers together first.

Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 4,818 times (of the 149,439 papers whose bibliographies we read); 1,061 citing papers with a page here →.

Updated 2 November 2026