On the Limitations of Embedding Based Methods for Measuring Functional Correctness for Code Generation

26 April 2024

Papers citing "On the Limitations of Embedding Based Methods for Measuring Functional Correctness for Code Generation"

8 / 8 papers shown

Title
The RealHumanEval: Evaluating Large Language Models' Abilities to Support Programmers Hussein Mozannar Valerie Chen Mohammed Alsobay Subhro Das Sebastian Zhao Dennis L. Wei Manish Nagireddy P. Sattigeri Ameet Talwalkar David Sontag ELM 30 18 0 03 Apr 2024
Unsupervised Evaluation of Code LLMs with Round-Trip Correctness Miltiadis Allamanis Sheena Panthaplackel Pengcheng Yin ALM OffRL LRM 43 9 0 13 Feb 2024
Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation Jiawei Liu Chun Xia Yuyao Wang Lingming Zhang ELM ALM 163 388 0 02 May 2023
Aligning Offline Metrics and Human Judgments of Value for Code Generation Models Victor C. Dibia Adam Fourney Gagan Bansal Forough Poursabzi-Sangdeh Han Liu Saleema Amershi ALM OffRL 25 12 0 29 Oct 2022
Grounded Copilot: How Programmers Interact with Code-Generating Models Shraddha Barke M. James Nadia Polikarpova 136 212 0 30 Jun 2022
Training language models to follow instructions with human feedback Long Ouyang Jeff Wu Xu Jiang Diogo Almeida Carroll L. Wainwright ... Amanda Askell Peter Welinder Paul Christiano Jan Leike Ryan J. Lowe OSLM ALM 301 11,730 0 04 Mar 2022
CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation Yue Wang Weishi Wang Shafiq R. Joty S. Hoi 196 1,451 0 02 Sep 2021
Measuring Coding Challenge Competence With APPS Dan Hendrycks Steven Basart Saurav Kadavath Mantas Mazeika Akul Arora ... Collin Burns Samir Puranik Horace He D. Song Jacob Steinhardt ELM AIMat ALM 189 614 0 20 May 2021