add a few NeurIPS'23 papers

README.md

@@ -70,10 +70,12 @@ Please feel free to send a pull request to add papers and relevant content that
 
 ## Code Completion
 
+- **CrossCodeEval: A Diverse and Multilingual Benchmark for Cross-File Code Completion** (2023), NeurIPS'23, Ding, Yangruibo, et al. [[pdf]](https://arxiv.org/abs/2310.11248)
+- **Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generation** (2023), NeurIPS'23, Liu, Jiawei, et al. [[pdf]](https://arxiv.org/abs/2305.01210)
 - **Domain Adaptive Code Completion via Language Models and Decoupled Domain Databases** (2023), arxiv, Tang, Ze, et al. [[pdf]](https://arxiv.org/pdf/2308.09313)
 - **RepoBench: Benchmarking Repository-Level Code Auto-Completion Systems** (2023), arxiv, Liu, T., et al. [[pdf]](https://arxiv.org/pdf/2306.03091)
 - **A Static Evaluation of Code Completion by Large Language Models** (2023), arxiv, Ding, Hantian, et al. [[pdf]](https://arxiv.org/pdf/2306.03203)
-- **Large Language Models of Code Fail at Completing Code with Potential Bugs** (2023), arxiv, Dinh, Tuan, et al. [[pdf]](https://arxiv.org/pdf/2306.03438)
+- **Large Language Models of Code Fail at Completing Code with Potential Bugs** (2023), NeurIPS'23, Dinh, Tuan, et al. [[pdf]](https://arxiv.org/pdf/2306.03438)
 - **RepoFusion: Training Code Models to Understand Your Repository** (2023), arxiv, Shrivastava, Disha, et al., [[pdf]](https://arxiv.org/pdf/2306.10998)
 - **LongCoder: A Long-Range Pre-trained Language Model for Code Completion** (2023), ICML'23, Guo, Daya, et al. [[pdf]](https://arxiv.org/pdf/2306.14893)
 - **R-U-SURE? Uncertainty-Aware Code Suggestions By Maximizing Utility Across Random User Intents** (2023), arxiv, Johnson, Daniel D, et al. [[pdf]](https://arxiv.org/pdf/2303.00732) 
@@ -719,6 +721,7 @@ Source Code Learning
 - [methods2test](https://github.com/microsoft/methods2test) (2022) - A supervised dataset consisting of Test Cases and their corresponding Focal Methods from a set of Java repositories
 - [ManyTypes4TypeScript](https://www.kevinrjesse.com/pdfs/ManyTypes4TypeScript.pdf) (2022) - Type prediction dataset for TypeScript 
 - [HumanEval](https://github.com/openai/human-eval) - Program synthesis from code comments
+- [HumanEval+](https://github.com/evalplus/evalplus) - Agumented HumanEval with sufficient tests and corrected reference solutions
 - [GitHub Code](https://huggingface.co/datasets/lvwerra/github-code) (2022) - 115M LoC in 32 programming languages
 - [D2A](https://arxiv.org/pdf/2102.07995.pdf) (2021) - A Dataset Built for AI-Based Vulnerability Detection Methods Using Differential Analysis
 - [CodeXGLUE](https://huggingface.co/datasets?search=code_x_glue) (2021)