Large Language Models Should Ask Clarifying Questions to Increase Confidence in Generated Code

25 August 2023

Papers citing "Large Language Models Should Ask Clarifying Questions to Increase Confidence in Generated Code"

8 / 8 papers shown

Title
Large Language Models are Better Reasoners with Self-Verification Yixuan Weng Minjun Zhu Fei Xia Bin Li Shizhu He Shengping Liu Bin Sun Kang Liu Jun Zhao ReLM LRM 53 214 0 19 Dec 2022
Large Language Models Can Self-Improve Jiaxin Huang S. Gu Le Hou Yuexin Wu Xuezhi Wang Hongkun Yu Jiawei Han ReLM AI4MH LRM 149 606 0 20 Oct 2022
Generating Natural Language Proofs with Verifier-Guided Search Kaiyu Yang Jia Deng Danqi Chen LRM 76 71 0 25 May 2022
Productivity Assessment of Neural Code Completion Albert Ziegler Eirini Kalliamvakou Shawn Simister Ganesh Sittampalam Alice Li Andrew Rice Devon Rifkin E. Aftandilian 151 181 0 13 May 2022
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation Yue Wang Weishi Wang Shafiq Joty Guosheng Lin 280 1,552 0 02 Sep 2021
Program Synthesis with Large Language Models Jacob Austin Augustus Odena Maxwell Nye Maarten Bosma Henryk Michalewski ... Ellen Jiang Carrie J. Cai Michael Terry Quoc V. Le Charles Sutton ELM AIMat ReCod ALM 180 1,925 0 16 Aug 2021
Evaluating Large Language Models Trained on Code Mark Chen Jerry Tworek Heewoo Jun Qiming Yuan Henrique Pondé ... Bob McGrew Dario Amodei Sam McCandlish Ilya Sutskever Wojciech Zaremba ELM ALM 205 5,454 0 07 Jul 2021
CodeBERT: A Pre-Trained Model for Programming and Natural Languages Zhangyin Feng Daya Guo Duyu Tang Nan Duan Xiaocheng Feng ... Linjun Shou Bing Qin Ting Liu Daxin Jiang Ming Zhou 151 2,613 0 19 Feb 2020