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InfiCoder-Eval: A Comprehensive Benchmark for Evaluating Code Large Language Models' Question-Answering Capabilities


Core Concepts
InfiCoder-Eval is a large-scale benchmark that systematically evaluates the free-form question-answering capabilities of code large language models across 15 programming languages and 5 major areas.
Abstract
InfiCoder-Eval is a novel benchmark created to comprehensively evaluate the question-answering abilities of code large language models (code LLMs). The benchmark comprises 234 carefully selected high-quality questions from Stack Overflow, covering 15 programming languages and 5 major areas: front-end, back-end, data science and machine learning, mobile and desktop, and IT operations. To address the challenge of evaluating free-form question responses, InfiCoder-Eval integrates four model-free metric types: keywords matching, blank filling, unit testing, and dialogue similarity. Domain experts annotate the detailed correctness criteria for each question, enabling automatic and efficient evaluation. The authors conduct a systematic evaluation of over 80 code LLMs on InfiCoder-Eval, leading to several insightful findings: GPT-4 achieves a score of 70.64%, outperforming the most capable open-source models, but is still far from perfect. At similar model sizes, coding LLMs are usually stronger than general LLMs, and fine-tuned LLMs are usually stronger than base LLMs. The performance differences between model families can be huge, highlighting the importance of training data and techniques. The scaling law is empirically verified for open-source models with fewer than 50B parameters, but not for those with more. InfiCoder-Eval is fully open-source and continuously maintained to foster more scientific and systematic practices for evaluating code LLMs.
Stats
The authors report that GPT-4 achieves a score of 70.64% on the InfiCoder-Eval benchmark. The best open-source model, deepseek-coder-33b-instruct, achieves a score of 62.96%. GPT-3.5-turbo achieves a score of 56.47%.
Quotes
"GPT-4 is still far from perfect, which is in contrast to the near 90% rate in HumanEval." "There is still a visible gap between open-source models and GPT-4." "The training techniques and training data are equally important or even more, helping to reduce the required scale for achieving certain score by more than 10× size." "Instruction-finetuning is critical for equipping the models with QA ability in the code domain."

Key Insights Distilled From

by Linyi Li,Shi... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2404.07940.pdf
InfiCoder-Eval

Deeper Inquiries

How can the InfiCoder-Eval benchmark be further expanded to cover an even wider range of real-world coding scenarios and question types?

Expanding the InfiCoder-Eval benchmark to cover a wider range of real-world coding scenarios and question types can be achieved through several strategies: Diversifying Question Types: Introduce new question types such as code optimization, algorithm design, code refactoring, and code review scenarios to capture a broader spectrum of coding challenges that developers face in real-world projects. Including Industry-Specific Questions: Incorporate questions from specific industries like finance, healthcare, e-commerce, and gaming to address domain-specific coding requirements and challenges. Adding Multi-Language Support: Extend the benchmark to include a more extensive range of programming languages beyond the current 15 languages to cater to the diverse language preferences of developers worldwide. Incorporating Multi-Platform Scenarios: Include questions that involve cross-platform development, cloud computing, IoT, and other emerging technologies to reflect the evolving landscape of software development. Introducing Collaborative Coding Scenarios: Incorporate questions that simulate collaborative coding environments, where multiple developers work together on a project, to assess the models' ability to handle teamwork and version control challenges. Expanding to Include Non-Technical Questions: Introduce questions that involve project management, software architecture, and other non-coding aspects of software development to evaluate the models' understanding of the broader context in which coding tasks are performed. By implementing these strategies, the InfiCoder-Eval benchmark can provide a more comprehensive evaluation of code LLMs across a wider range of real-world coding scenarios and question types.

What are the potential biases and limitations of using Stack Overflow questions as the data source for the benchmark, and how can they be mitigated?

Using Stack Overflow questions as the data source for the benchmark may introduce several biases and limitations: Selection Bias: Stack Overflow questions may not represent the full spectrum of coding challenges faced by developers, as they are influenced by the preferences and expertise of the Stack Overflow community. To mitigate this bias, questions can be sourced from multiple platforms and coding forums to ensure diversity. Quality Bias: Stack Overflow questions vary in quality, with some being poorly formulated or lacking context. Domain experts should carefully curate and filter questions to ensure that only high-quality and relevant questions are included in the benchmark. Language Bias: Stack Overflow is predominantly English-centric, which may lead to a bias towards English-speaking developers. To address this bias, questions in multiple languages should be included to provide a more inclusive evaluation. Topic Bias: Stack Overflow questions may be skewed towards popular programming languages and technologies, potentially neglecting niche or emerging areas of software development. To mitigate this bias, questions should cover a wide range of programming languages and domains. Answer Bias: The benchmark's reliance on Stack Overflow answers as reference points may introduce bias towards specific coding solutions. Domain experts should ensure that evaluation criteria are objective and consider multiple valid approaches to coding problems. By being aware of these biases and limitations and taking proactive steps to address them through careful curation, diversity in question selection, and validation by domain experts, the impact of using Stack Overflow questions as the benchmark data source can be minimized.

How can the insights from the InfiCoder-Eval evaluation be leveraged to guide the development of more capable and trustworthy code large language models?

The insights from the InfiCoder-Eval evaluation can be leveraged to enhance the development of code large language models in the following ways: Model Improvement: Identify specific weaknesses and strengths of existing models based on their performance in the benchmark. Use this information to guide model refinement, focusing on areas where models struggle and enhancing capabilities where they excel. Training Data Enhancement: Analyze the types of questions that models perform well on and those they struggle with. Use this information to enrich training data with diverse and challenging coding scenarios to improve model performance across a wider range of tasks. Instruction-Finetuning Emphasis: Recognize the importance of instruction-finetuning in improving QA ability in the code domain. Encourage the incorporation of instruction-finetuning phases in model development to enhance models' ability to follow specific coding instructions accurately. Bias Mitigation: Address biases and limitations identified during the evaluation process to ensure that models are developed in a fair and unbiased manner. Implement strategies to mitigate biases in training data, evaluation criteria, and model development processes. Scaling Strategies: Consider the scaling laws observed in the evaluation to guide the development of models beyond 50B parameters. Investigate the potential barriers to scaling beyond this point and explore alternative training techniques to achieve optimal performance. By utilizing the insights gained from the InfiCoder-Eval evaluation to inform model development, training data enhancement, bias mitigation, instruction-finetuning emphasis, and scaling strategies, developers can create more capable and trustworthy code large language models that better meet the needs of developers in real-world coding scenarios.
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