Comprehensive list of LLM benchmarks: Part 2 -Coding benchmarks
As the boundaries of artificial intelligence continue to expand, large language models (LLMs) are revolutionizing the software development landscape. With their ability to write code, generate commit messages, and even debug errors, LLMs are poised to become an indispensable tool in every developer’s arsenal. However, with numerous models vying for attention, selecting the right one for your specific needs can be a daunting task. That’s where benchmarks come in — standardized measures that help evaluate the performance of LLMs in various software development tasks. In this post, we’ll delve into the world of LLM benchmarks, exploring the key metrics that matter, and providing a comprehensive comparison of the most popular benchmarks used to rank LLMs for software development. By the end of this article, you’ll be equipped with the knowledge to make informed decisions about which LLM is best suited to augment your development workflow.
Please refer my blog post “Understanding LLM Benchmarks” to know
1. What Are LLM Benchmarks?
2. Why are LLM benchmarks important?
3. What happens in the benchmarking process?
4. What are the performance metrics and evaluation criteria tracked for the benchmarks?
Popular Benchmarks for Large Language Models in Software Development
HumanEval:
HumanEval is a benchmark created by OpenAI to test the abilities of large language models (LLMs) in software development. It was introduced in July 2021 and consists of 164 programming challenges in Python, along with unit tests to verify the results.
What makes HumanEval unique is that it focuses on whether the code generated by the model actually works as intended, rather than just checking for text similarity. The challenges are written in a way that ensures they’re not part of any training dataset, making it a more realistic test for LLMs.
The benchmark evaluates the LLM’s response by checking whether it passes the corresponding unit tests. It uses a metric called Pass@k, which measures the rate of successfully passing the provided unit tests. With more powerful models, it’s common to evaluate with Pass@1, giving the LLM only one chance to solve each challenge.
HumanEval helps identify models that can truly solve problems, rather than just regurgitating code they’ve been trained on. While it was initially introduced for Python, the community has since created versions for other programming languages.
ClassEval: A Benchmark for Code Generation in Python Classes
ClassEval is a more recent benchmark, released in August 2023, that focuses on code generation in Python classes. It consists of 100 Python classes with coding tasks, challenging models to generate code that spans the logic of an entire class, not just a single function.
Each class has an average of 33.1 test cases to verify the results. The tasks cover a wide range of topics, including management systems, data formatting, and game development. ClassEval also takes into account library, field, and method dependencies, making it a more comprehensive test for LLMs.
By using ClassEval, developers can evaluate the abilities of LLMs in generating code that’s more complex and realistic, rather than just focusing on single functions.
SWE-bench: A Real-World Challenge
SWE-bench, released in October 2023, is a comprehensive benchmark that contains over 2000 real-world GitHub issues and PRs from 12 popular Python repositories. This benchmark presents a unique challenge to LLMs, requiring them to understand issue descriptions and coordinate changes across multiple functions, classes, and files. The models must interact with execution environments, process broad contexts, and perform reasoning that surpasses the level of code generation tasks found in most benchmarks. The changes made by the model are then verified with unit tests that were introduced when the actual issues were resolved.
Aider: Pair Programming with LLMs
Aider, an open-source solution, allows users to utilize models as a pair programmer in their terminal. The Aider core application conducts benchmarks to help users choose between the vast majority of models becoming available. The assessment focuses on how effectively LLMs can translate natural language coding requests into code that executes and passes unit tests. The benchmark includes two types of tasks: code editing and code refactoring.
The code editing tasks consist of 133 small coding problems based on Exercism Python exercises, with models’ responses validated using complementing unit tests. The code refactoring tasks, totaling 89, represent non-trivial refactorings from 12 popular Python repositories, with correctness roughly checked using one unit test per refactoring. Aider offers a leaderboard to track the progress of various models.
BigCodeBench: A Comprehensive Evaluation
BigCodeBench, a recently released benchmark, provides a comprehensive evaluation of LLMs in code generation. By testing models on a diverse range of tasks, BigCodeBench aims to identify their strengths and weaknesses, ultimately guiding the development of more accurate and efficient models.
DS-1000: Real-World Data Science Use Cases
DS-1000, released in November 2022, is a Python benchmark of data science use cases based on real StackOverflow questions. The benchmark contains 1000 problems that span 7 popular Python data science libraries, aiming to reflect a diverse set of realistic and practical use cases. The problems are slightly modified compared to the originals on StackOverflow to prevent models from memorizing correct solutions through pre-training.
MBPP: Mostly Basic Python Problems
MBPP, developed by Google, contains about 1000 crowd-sourced Python programming problems that are simple enough to be solved by beginner Python programmers. The dataset covers programming fundamentals, standard library functionality, and more, with each task accompanied by a description, a code solution, and 3 automated test cases.
APPS: Advanced Problem-Solving
APPS, released in May 2021, contains 10,000 problems sourced from a variety of open-access coding websites. The goal of APPS is to mirror the evaluation of human programmers, presenting coding problems in natural language. The dataset contains a wide range of problems, from beginner-level tasks to advanced competition-level problems, with an average of 13.2 test cases per example.
CodeXGLUE: A General Language Understanding Evaluation
CodeXGLUE, released in February 2021, is a General Language Understanding Evaluation benchmark for CODE. It contains 14 datasets for 10 programming-related tasks, covering many scenarios, including code-code, text-code, code-text, and text-text tasks. The benchmark aims to evaluate the capabilities of LLMs in a wide range of programming languages, including C/C++, C#, Java, Python, CSS, PHP, JavaScript, Ruby, and Go.
Large Language Model Benchmarks for Code Generation: Challenges and Opportunities
As we explored the various benchmarks designed to evaluate the performance of large language models (LLMs) in code generation, it became clear that while significant progress has been made, there are still several challenges that need to be addressed. Let’s discuss the shortcomings of current benchmarks and the opportunities for improvement.
Programming Language Diversity
One of the most notable limitations of current benchmarks is the focus on Python. While Python is a popular language in the machine learning community, there are many other programming languages used in the industry. To truly evaluate the capabilities of LLMs, benchmarks must be developed to cover a diverse range of languages. The ultimate goal should be to have a single standard benchmark that can inform developers about the best model for their language of choice.
Number of Examples
While having a large number of evaluation examples is desirable, it can be costly and impractical. There needs to be a balance between having enough examples to evaluate a model’s capabilities and not having so many that they become redundant. The “HumanEval” benchmark, for example, includes an example that simply requires adding two numbers, highlighting the need for careful curation of examples.
Verification and Scoring
Most benchmarks rely on unit tests to verify model results, but this approach has its limitations. Unit tests may be incomplete or incorrect, and they do not assess the quality of the generated code. A more comprehensive evaluation should consider factors such as code design, naming, comments, documentation, and optimization. Additionally, meta-metrics like model cost and open-source status should be factored into the assessment.
Task Diversity
While code generation is a critical aspect of software development, it is only one part of the larger picture. Software developers need a range of skills, including writing and reviewing code, refactoring, planning, testing, and maintaining documentation and services. Benchmarks should be developed to evaluate LLMs across a broader range of tasks to better reflect the complexities of software development.
Stale State
Most benchmarks are curated once and then considered “done.” However, software development is a rapidly evolving field, with new tools and techniques emerging constantly. Benchmarks should be dynamic and continually updated to reflect the latest developments and provide an accurate picture of the best models to use.
In conclusion, while significant progress has been made in developing benchmarks for LLMs in code generation, there are still several challenges that need to be addressed. By acknowledging these limitations and working to develop more comprehensive and dynamic benchmarks, we can create a more accurate and informative evaluation of LLMs in software development.