In addition, we discuss challenges and opportunities regarding the gap. We have already seen it being superior to GPT-4 on coding tasks, scoring a whopping a 71. 0% on GSM8k grade-school math problems, demonstrating its advanced computational skills. Although it MMLU (Massive Multitask Language Understanding) benchmark is good, HumanEval shows coding capability is quite a bit lower compared to StarCoder (33. 0% and it gets an 88% with Reflexion, so open source models have a long way to go to catch up. 2% on the Codex Human Level Python coding test compared to Claude 1. In the GSM8K grade-school maths problems benchmark , Claude Instant 1. Make sure to use python 3. As reported by DecryptAnthropic’s Claude was designed with a unique “constitution,” a set of rules inspired by the Universal Declaration of Human Rights,. Claude-2 wins. This represents a significant advancement compared to Claude 1. We find that Codex matches or even exceeds its. 2% on Codex HumanEval. It can also handle other programming languages such as Java, C++, and HTML. The prompt provided to the model is shown. APPS 是 Hendrycks 等人提出的用来衡量语言模型编程能力的数据集，APPS一共包含10000个编程问题，每个编程问题都有若干个 unit tests，其中5000个编程问题作为训练集，5000个编程问题作为测试集，训练集中的每个问题还包括若干个正确答案。HumanEval is just one data point, and it's an incresingly irrelevant one. We found that the Codex model achieved above 80% coverage for the HumanEval dataset, but no model had more than 2% coverage for the EvoSuite SF110 benchmark. , 2021)—developed by OpenAI for e valuating Codex—and other bench- 2 T able 1: Large pre-trained language models related to programming languages in the literature. 5% on the multiple-choice section of the Bar exam. 2% up from 56. It scored 71. Claude 2 also showcased enhanced coding skills, achieving an impressive score of 71. 0 percent up from 85. . To help standardize the evaluation of multilingual code generation and translation, we develop and release the HumanEval-X Benchmark. HumanEval-X for Realistic Multilingual Benchmarking. 7 tests per problem. The evaluation covered a wide range of programming languages and yielded impressive results, helping to quantify the model’s performance in. 8% at k=10 and 72. Pass rates of our models on the HumanEval dataset as a function of model size. OpenAI claims the largest Codex model it developed, which has 12 billion parameters, can solve 28. However, a major challenge for this task is to select. 0 percent on the Codex HumanEval, a Python coding test. , 2021 ) and APPS (Hendrycks et al. et al. A core component of this project was developing infrastructure and optimization methods that behave predictably across a. 7 tests per problem. In addition to predicting final loss, we developed methodology to predict more interpretable metrics of capability. En framtida studie skulle kunna träna Codex för Terraform med OpenAI:s API eller skapa en Codex-kopia genom att träna GPT-3 kopian OPT som i sin tur kan bli tränad för Terraform. 69. See a full comparison of 50 papers with code. We evaluated the models based on compilation rates, test correctness, coverage, and test smells. This repo also attempts to evaluate and reproduce performance results of existing LLMs for code, such as Llama, Alpaca and CodeAlpaca for code generation benchmarks (HumanEval and MBPP). HumanEval: Hand-Written Evaluation Set . 7 tests per problem. Trained on TPU-v4. 2. 2% up from 56. Make sure to use python 3. 3 in various evaluations, achieving impressive scores on Codex HumanEval and GSM8k. CodeGeeX is pre. We present new benchmarks on evaluation code generation models: MBXP and Multilingual HumanEval, and MathQA-X. side Codex [7], HumanEval is a benchmark for Python to assess the functional correctness of programs generated by code gener-ation models. Eval+ in particular adds thousands of test cases to the same 163 problems in HumanEval to cover more edge cases. g. We would like to show you a description here but the site won’t allow us. , 2021) as an example, Codex has a pass @ 100 @ 100 @100 @ 100 (pass if one or more among 100 100 100 100 generated solutions for a given problem can pass the corresponding test cases) of 77. Our extensive experiments suggest that CodeGeeX outperforms. 2%, up from 56. Notably, all the mentioned models generate code solutions for each problem utilizing a single attempt, and the resulting pass rate percentage is reported. It measures the performance of code generation models on almost 200 coding challenges. This hinders progress, given that the expensive compute resources required to. 4%. This is compared to 67% of GPT-4. 0% of the older version. just announced their own LLaMa style code LLM at their developer day! replit-code-v1-3b - 2. 2% on the Codex HumanEval Python coding test, up from 56. g. proposed such as Codex (Chen et al. 2 percent on the Codex HumanEval benchmark, up from 56 percent. 7 or later: See moreCodex is a GPT language model fine-tuned on code from GitHub, and it can generate Python code from docstrings. • Claude 2 achieved a 71. Separate groups are balanced (each open brace is properly closed) and. HumanEval-X: 多语言代码生成基准 . , GPT-4, ChatGPT and CodeGen), across different model types and sizes, and find that surprisingly the pass@ k on the new dataset is on average 15. Make sure to use python 3. It scored a C+ 76. 1: 26. That’s a significant improvement over prior models, which achieved a score of 56. 8% of the problems, while GPT-3 solves 0% and GPT-J solves 11. 0%. Compared with the widely-used HumanEval benchmark from OpenAI, CoderEval can be used to assess the performance of models against pragmatic code generation beyond just generating standalone functions. SkyCode是一个多语言开源编程大模型，采用GPT3模型结构，支持Java, JavaScript, C, C++, Python, Go, shell等多种主流编程语言，并能理解中文注释。模型可以对代码进行补全，拥有强大解题能力，使您从编程中解放出来，专心于解决更重要的问题。| SkyCode is an open source programming model, which adopts the GPT3 model structure. 2022. An interesting aspect of StarCoder is that it's multilingual and thus we evaluated it on MultiPL-E which extends HumanEval to many other languages. 2% to 88. HumanEval-X consists of 820 high-quality human-crafted data samples (each with test cases) in Python, C++, Java, JavaScript, and Go, and can be used for various tasks. This goes to show how effective it is when it comes to writing computer codes. 2022. HumanEval-X for Realistic Multilingual Benchmarking. The important distinction is whether your data contains proper word boundaries and rigorous translation references. 0%, on the Codex HumanEval, a Python coding test. On GSM8k, a large set of. 2%, up from 56. 0% on GSM8k grade-school math problems, revealing. 2% on the Codex HumanEval Python coding test and 88% on GSM8k grade-school math problems, showcasing its advanced computational skills. For example, our latest model scored a 71. We found that the Codex model achieved above 80% coverage for the HumanEval dataset, but no model had more than 2% coverage for the Evo-Suite SF110 benchmark. CodeGen2. AI Chatbots Like ChatGPT and Google Bard Don’t Meet EU Law Standards: Study. 2% on the Codex HumanEval Python coding test and an 88. The generated tests also suffered from test smells, such as. In a Python coding test called Codex HumanEval, Claude Instant 1. When a single sample is generated for each problem, GPT-12B solves no problems, but Codex (fine-tuned on code) solves 28. 2% up from 56. It consists of 164 hand-written programming prob-lems and solutions in Python, each of which includes a function signature, docstring, body, and multiple unit testsThe HumanEval dataset is a collection of Python problems, each in the same format as the example above. We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. 2% on the Codex HumanEval, a Python coding test, up from 56. Improved coding skills: Claude 2 has significantly improved coding skills, achieving a score of 71. Select Online Assignment from the list of assignment types when it. 2%. 虽然 Codex 能为大多数 HumanEval 问题抽取正确的解决方案，但我们发现它有一些局限性。首先，Codex 的训练样本效率不高，我们的训练数据集包含 GitHub 上公开可用的 Python 代码的很大一部分，总计数亿行代码。. 0% on the Codex HumanEval, a Python coding test. It consists of 164 hand-written programming problems and solutions in Python, each of which includes a function signature, docstring, body, and multiple unit. On the other hand, there are several open-source Code LLMs available. Figure 1. 3's score of 56. 0% on the extensive collection of grade-school math questions in GSM8k. g. Availability: Claude 2 is available in beta starting in the U. e. Bottom: unit tests. This is an evaluation harness for the HumanEval problem solving dataset described in the paper "Evaluating Large Language Models Trained on Code". 3. Impressive Python coding skills, scoring 71. On GSM8k, a large set of grade-school math problems, Claude 2 scored 88. HumanEval-X: 多语言代码生成基准 . The new Claude also comes with some very exciting stats about it: the AI model scored a 76. jsonl and example_solutions. , HumanEval, MBPP,. 0%. arXiv:2206. Hi, we reproduced the performance of the raw GPT-Neo (125M and 1. the results on Multilingual HumanEval and can also be found in Appendix D. 3 model has a score of 56. PyCodeGPT is efficient and effective GPT-Neo-based model for python code generation task, which is similar to OpenAI Codex, Github Copliot, CodeParrot, AlphaCode. 3. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. A distinct production version of Codex powers GitHub Copilot. 0% up from 85. , 2021) and MBPP benchmark (Austin et al. 作者有提到不管是在GPT-3的预训练模型训练，还是从头开始训练得到的模型，在精度上基本. 5% in the Bar exam's multiple-choice section (GPT-3. It consists of 820 high-quality human-crafted data samples (each with test cases) in Python, C++, Java, JavaScript, and Go, and can be used for various tasks, such as code generation and translation. Ensure that the task_id used matches the task_id from the desired benchmark. Model versions. ,2021)—which is a dataset of 164 hand-written problems in python with associated unit tests—the functional correct-ness metric of pass@k (where k code samples are generated per problem and a problem is consid-ered solved if any of the k generations passes theSince HumanEval only evaluates the natural language to Python synthesis, we curate an unseen evaluation dataset in each of the 12 languages, to evaluate the perplexity of different models. We also include the cached outputs from executing the groundtruth SQL queries. 图2 HumanEval数据集中的三个编程问题例子. However, a major challenge for this task is to select. You signed out in another tab or window. in each of the 12 languages, to evaluate the perplexity of different models. However since line-based evaluations do. Eval+ is an expanded version of OpenAI’s official standardized programming benchmark, HumanEval - first introduced in their Codex paper. 17, and 0. Claude 2 is also significantly safer. We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. 图2 HumanEval数据集中的三个编程问题例子. 3’s 85. 2％のスコアを持っています。その前身であるクロード1. Our extensive experiments suggest that CodeGeeX outperforms multilingual code models of similar scale for both the tasks of code generation and translation on HumanEval-X. Ordered version of string, is a string where all words (separated by space) are replaced by a new word where all the characters arranged in ascending order based on ascii value. metallicamax • 6 mo. Large pre-trained code generation models, such as OpenAI Codex, can generate syntax- and function-correct code, making the coding of programmers more productive and our pursuit of artificial general intelligence closer. 2% increase in the gap clearly shows that the coding skill of the Claude 2 model is better. 0 percent on the Codex HumanEval, a Python coding test. Each problem is accompanied by a task ID, a prompt, the canonical solution, and unit tests. 3. ,2020). 2%, which is much higher than 56. HumanEval CodeGeeX-13B Pass@1 22. And it seems the model is quite proficient at math too: on GSM8k, a large set of grade-school math problems, Claude 2 scored 88. We present two new benchmarks, MBXP and Multilingual HumanEval, designed to evaluate code generation models in over 10 programming languages. We have weighted the overall contribution from each of these five datasets equally. AWS, GCP eller Azure. We have an exciting roadmap of capability improvements planned for Claude 2 and will be slowly. jsonl under data to illustrate the format and help with debugging. Claude 2. WizardLM - Family of instruction-following LLMs powered by Evol-Instruct: WizardLM, WizardCoder and WizardMath. , 2022) and InCoder (Fried et al. 4%. An illustration of tasks supported by HumanEval-X. Each problem includes a function signature, docstring, body, and several unit tests, with an average of 7. 5 %. 5, Codex, and CodeGen to generate unit tests for competitive programming assignments from the extended version of the HumanEval dataset created by the AWS AI Labs [17] as well as 47 open-source projects from the EvoSuite SF110 benchmark dataset [13]. On your course’s homepage, click Assignments (left sidebar) and then Create Assignment (bottom right). Moreover, it can perfectly carry out PDF tasks, something which GPT 4 struggles with. , 2022), PaLM (Chowdhery. 8% over the code-davinci-002 model, and an absolute improvement of more than 20% over the previous state-of-the-art results. - Claude 2 scored a 71. 5% on the multiple choice section of the Bar exam, an increase from 73%. We found that the Codex model achieved above 80% coverage for the HumanEval dataset, but no model had more than 2% coverage for the Evo-Suite SF110 benchmark. We additionally include results reported by prior works. Max tokens: 100K. To help standardize the evaluation of multilingual code generation and translation, we develop and release the HumanEval-X Benchmark. When we omit the. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28. 3. , 2021). HumanEval only evaluates the natural language to Python synthesis, we curate an unseen evaluation dataset3 in each of the 12 languages, to evaluate the perplexity of different models. 2 to 88. Intended Use and Limitations As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them. (2021). And Claude 2 scored 76. When it comes to writing, Llama-2 and GPT-4 are very different, too. 5: 41. An interesting aspect of StarCoder is that it's multilingual and thus we evaluated it on MultiPL-E which extends HumanEval to many other languages. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28. On HumanEval, a new evaluation set, functional correctness is measured for synthesizing programs from docstrings. On GSM8k, a large set of grade-school math problems, Claude 2 scored 88. Claude 2 scored a 71. Installation . , GPT-4 and ChatGPT) demonstrates that HumanEval+ is able to catch significant amounts of previously undetected wrong code synthesized by LLMs, reducing. We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. On GSM8k, a large set of grade-school math problems, Claude 2 scored 88. 2% (up from 56. 6 test cases allocated to each problem. Increased safety — Claude 2 was 2x better at giving harmless responses compared to Claude 1. A distinct production version of Codex powers GitHub Copilot. While GPT-4 is considerably better than GPT-3. 0%. Also, it scored 88. The generated tests also suffered from test smells, such as. This temperature is very important for sampling diverse outputs, as is mentioned in the original codex paper. Since ChatGPT has any specialized coding or mathematical ability, it frequently fails to generate accurate or coherent results. We provide example_problem. Google has proposed PaLM-Coder [3]. En cuanto a las capacidades de codificación, Claude 2 demostró un aumento informado en la competencia. 5 with 7B is on par with >15B code-generation models (CodeGen1-16B, CodeGen2-16B, StarCoder-15B), less than half. HumanEval. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model. 98\%$ for HumanEval using between 1 to 5 simulated user queries. We find that although Codex is allegedly focused on Python ([10] §3. Do you have any plans to publish the raw GPT-Neo on HumanEval? In addition, are there any tricks in the process of reproducing this? Thanks! Our re-produce results:Codex davinci-002 Introductory Pass@1 29. 1We report the results on the HumanEval benchmark with the Codex model code-cushman-001. 2. 0% on GSM8k grade-school math problems, revealing its advanced computational skills. This. Codex 模型参数从12M到12B不等，是目前最强的编程语言预训练模型。Codex 能够帮助程序员根据函数名和注释自动补全代码、直接生成代码、自动补充测试样例，并支持多种编程语言。本期 Azure OpenAI 官方指南将详解 Codex 的模型结构如何帮助程序员实现自动代码生成。We introduce Codex, a GPT language model fine-tuned on publicly available code from GitHub, and study its Python code-writing capabilities. Choosing the Right Model The choice of model largely depends on the specific requirements. 2% up from 56. One commonly used Python benchmark is HumanEval, which assesses whether the model can complete functions based on their signature and docstring. A distinct production version of Codex powers GitHub Copilot. However, a major challenge for this task is to select. 3’s score of 56. 2% up from 56. 4 77. To evaluate the functional correctness of Codex, a set of 164 programming problems was used, called the HumanEval dataset. We apply SCoT prompting to two LLMs (i. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model solves 28. City of Heroes Demos and Movies. It was discovered that both StarCoder and StarCoderBase outperformed the largest models, such as PaLM, LaMDA, and LLaMA, despite their significantly smaller size. on the web for free with limited use and via a paid API (in limited access). En el examen de codificación Codex HumanEval, Claude 2 obtuvo una puntuación del 71. from typing import List def separate_paren_groups (paren_string: str) -> List [str]: """ Input to this function is a string containing multiple groups of nested parentheses. It enables users to upload as many as 100k data tokens which Anthropic says is. 2021) and InCoder (Fried et al. Anthropic is working to make Claude more globally available. The generated tests also suffered from test smells, such as Duplicated Asserts and Empty Tests. Here is nearly functional example code (you just have to. 3, which scored only 56. 3’s score of 85. 0% obtenido por Claude 1. Its predecessor, the Claude 1. 9. This paper introduces CodeGeeX, a multilingual model with 13 billion parameters for code generation, and develops the HumanEval-X benchmark for evaluating multilingual models by hand-writing the solutions in C++, Java, JavaScript, and Go. 2%, up from 56. It implements the evaluation harness for the HumanEval problem solving dataset described in the paper "Evaluating Large Language Models Trained on Code". HumanEval consists of 164 hand-written problems, each of which includes a function signature, a docstring, a canonical reference function, and multiple unit tests. 7% of the problems. 2% on the Codex HumanEval Python coding test. Spider includes the evaluation script and the data. . To validate the performance of these models, multiple existing benchmarks (e. On HumanEval, a new evaluation set we release to. Additionally, on GSM8k, a. 3. Claude-2 wins. [3] creates the HumanEval benchmark and evaluates the Codex model, which solves 27% of the problems. Home : CoH Demo Info : CoH Demo Content Resources CoH Demo Content Resources. It measures the performance of code generation models on almost 200 coding challenges. In comparison, GPT-4 score is 4. , 2021). I also strongly suggest reading this thread and the code evaluation benchmark at HF. On GSM8k, a large set of grade-school math problems, Claude 2 scored 88. 2%. Our extensive evaluation across 26 popular LLMs (e. It also improved to 88% accuracy on grade school math problems. GPT-4 is a big upgrade of foundation model capability, e. Our benchmarks also support other code completion tasks such as code insertion or translation in many languages. 1 and 4. training. 4\% 77. Anthropic said its chatbot scored a 71. 1 HumanEval Dataset For our experiment, we use the HumanEval dataset [3]. HumanEval-X is a new multilingual benchmark that contains 820 human-crafted coding problems in 5 programming languages (Python, C++, Java, JavaScript,. We have an exciting roadmap of capability improvements planned for Claude 2 and will be slowly and. Advanced Computational Skills: Claude 2 also scored a 71. Codex can also make mistakes binding operations to variables, especially when the. 0%,. 3. This is an evaluation harness for the HumanEval problem solving dataset described in the paper "Evaluating Large Language Models Trained on Code". 1. 5% on the multiple choice section of the Bar exam, up from 73%. The HumanEval Dataset "HumanEval" refers to a hand-crafted dataset comprising 164 programming challenges. 2%, up from 56. 3は、これらのテストで56％のスコアしか出していない。It scored 71. Codex, LaMDA, GLaM, PaLM, Gopher, Jurassic-1, and Chinchilla [Brown et al. To help standardize the evaluation of multilingual code generation and translation, we develop and release the HumanEval-X Benchmark. Our WizardCoder generates answers using greedy decoding and tests with the same codeunveiled Codex [16] and Code-Davinci [38]. 0% . StarCoder and comparable devices were tested extensively over a wide range of benchmarks. All models are evaluated on the HumanEval dataset that consists of 164 prompts with description in the form of code, comments, etc. We use MultiPL-E to extend the HumanEval benchmark (Chen et al. Model performance on MultiPL-HumanEval by language frequency and type-checking. 0%) on the Codex HumanEval, a Python coding test. 1 IntroductionWhile EvalPlus is general, we extend the test-cases of the popular HumanEval benchmark by 80x to build HumanEval+. We used ChatGPT 3. 8% of the problems with just a single sample from a 12-billion-parameter model. 5 (ChatGPT) at analyzing Solidity, it is still missing key features, such as the ability to reason about cross-function reentrancy and inter-function relationships in general. 0% on GSM8k grade-school math problems. Recently, DS-1000 [16] HumanEval-X for Realistic Multilingual Benchmarking. 2% score on the Codex HumanEval, a Python coding test, up from 56. Claude 2 has apparently improved its coding skills, scoring 71. HumanEval-X is a new multilingual benchmark that contains 820 human-crafted coding problems in 5 programming languages (Python, C++, Java, JavaScript,. On the Codex HumanEval, an evaluation designed to assess Python coding skills, Claude-2 achieved an impressive score of 71. In this task, the model is trained to predict whether a token is a code identifier, forcing the model to learn code syntax and data flow. 2%. 🌐 English . son of all existing models on the HumanEval benchmark. , in code and math, accompanied by a much higher. We maintain a public fork of the NeoX repository here, which includes the (minor) changes we made to the codebase to allow for tabs & newlines in the tokenization, and also includes instructions for running the perplexity and HumanEval tasks. This extension is made possible by performing large-scale. 17, and 0. 2%). Building upon HumanEval (Python only), we develop the HumanEval-X benchmark for evaluating multilingual models by hand-writing the solutions in C++, Java, JavaScript, and Go. On GSM8k, a large set of grade-school math problems, Claude 2 scored 88. 0%) and CodeT: Code Generation with Generated Tests (65. 2% score on the Codex HumanEval, a Python coding test, up from 56. We use MultiPL-E to extend the HumanEval benchmark and MBPP benchmark to 18 languages that encompass a range of programming paradigms and popularity. On GSM8k, a large set of grade-school math problems, Claude 2 scored 88. 3. 8% of the problems, while GPT-3 solves 0% and GPT-J solves 11. 2% on the Codex HumanEval, a Python coding test. The proposed Codex solves 28. 在代码生成领域，当前最广泛被使用的是OpenAI在Codex论文中开源的HumanEval，该基准测试集由164道由OpenAI工程师手动编写的编程任务组成，以一定. Finally, since HumanEval only evaluates the natural language to Python synthesis, we curate an unseen evaluation dataset 3 3 3 The exact training set that Codex was trained on is unknown. HumanEval-X consists of 820 high-quality human-crafted data samples (each with test cases) in Python, C++, Java, JavaScript, and Go, and can be used for various. We show that measuring uncertainty in natural language is challenging because of "semantic equivalence" -- different sentences can. MuTAP starts by calling an initial prompt on LLM (Codex and llama-2-chat) to generate test cases for a Program Under Test (PUT). LLMs like Codex Chen et al. Anthropic has been working to improve the underlying safety of Claude 2, making it more harmless and harder to prompt to produce offensive or. On HumanEval, a new evaluation set we release to measure functional correctness for synthesizing programs from docstrings, our model. 2% on the Codex HumanEval for assessing Python coding skills, up 15 percentage points from Claude 1. Figure 1: (left) We show the overall ability of a 52B language model to evaluate its own proposed answers (sampled at unit temperature) to questions from TriviaQA, Lambada, Arithmetic, GSM8k, and Codex HumanEval. 5% on MBPP. 0% on the Codex HumanEval, a Python coding test. These datasets are generated using a conversion framework that transpiles prompts and test cases from the original MBPP and HumanEval datasets into the corresponding data in the target language. 2% up from 56. lm-evaluation-harness is undergoing a Big Refactor right now which. According to Anthropic, Claude 2 scored 71. It consists of 820 high-quality human-crafted data samples (each with test cases) in Python, C++, Java, JavaScript, and Go, and can be used for various tasks, such as code generation and translation. On a data science benchmark called DS-1000 it clearly beats it as well as all other open-access. What I’ve found using GPT-4 for help coding is that you really need to know a little bit about programming to know what to ask and how to ask. , 2021), CodeGen (Nijkamp et al. Released alongside Codex, HumanEval is a benchmark to measure code generation models on the functional correctness of programs synthesized from docstrings (Chen et al. Llama 2 scored 71. Claude 2 also scored a 71. 2% on the Codex HumanEval Python coding test and an 88. Each problem included a function signature, docstring, body, and multiple unit tests, with an average of 7. side Codex [7], HumanEval is a benchmark for Python to assess the functional correctness of programs generated by code gener-ation models. To ensure a thorough assessment of the functional correctness of LLM-synthesized code, HumanEval+ extends the number of test cases significantly, averaging at 774. I haven’t played much with the most recent Codex, but I need to investigate again. 0%. HumanEval: Hand-Written Evaluation Set . Our extensive evaluation across 26 popular LLMs (e. Possibilities: Claude's insane 100k context window allows for hundreds of pages to be analyzed. Three example problems from the HumanEval dataset, where the probabilities that a single sample from Codex-12B passes unit tests are 0.