Evaluating Large Language Models' Coding Proficiency Beyond Standard Benchmarks

Core Concepts

Existing coding benchmarks have limitations in comprehensively evaluating the program synthesis abilities of large language models (LLMs). EVOEVAL, a new benchmark suite, is introduced to evolve existing problems into more diverse and challenging domains to better assess LLM coding capabilities.

Abstract

The paper introduces EVOEVAL, a program synthesis benchmark suite created by evolving existing HUMANEVAL problems. The key insights are:

Existing coding benchmarks like HUMANEVAL have limitations - they contain a limited number and variety of problems, and are prone to data leakage issues. This raises questions about the reliability and comprehensiveness of evaluating LLM coding abilities using these benchmarks.

EVOEVAL uses targeted transformation prompts to evolve HUMANEVAL problems into new problems across 5 semantic-altering (Difficult, Creative, Subtle, Combine, Tool Use) and 2 semantic-preserving (Verbose, Concise) benchmarks. This generates a more diverse and challenging set of 828 problems.

Comprehensive evaluation on 51 LLMs shows a significant drop in performance (39.4% on average) when moving from HUMANEVAL to EVOEVAL. The performance drop is not uniform across LLMs, leading to drastic ranking changes.

Instruction-following LLMs are more sensitive to subtle changes in problem descriptions compared to their base counterparts, indicating potential overfitting to existing benchmarks.

LLMs struggle with composing known concepts to solve more complex problems, highlighting the need for benchmarks that test compositional generalization abilities.

Overall, EVOEVAL provides a more comprehensive and challenging benchmark suite to better evaluate the true program synthesis capabilities of LLMs.

Stats

HUMANEVAL contains 164 problems, with an average of 9.6 test cases per problem.
EVOEVAL contains 828 problems across 7 different benchmarks, with an average of 49.8 test cases per problem.

Quotes

"Is the leaderboard performance on existing benchmarks reliable and comprehensive enough to measure the program synthesis ability of LLMs?"
"EVOEVAL not only provides comprehensive benchmarks, but can be used to further evolve arbitrary problems to keep up with advances and the ever-changing landscape of LLMs for code."

Key Insights Distilled From

Top Leaderboard Ranking = Top Coding Proficiency, Always? EvoEval

by Chunqiu Stev... at arxiv.org 03-29-2024

https://arxiv.org/pdf/2403.19114.pdf

Deeper Inquiries

How can EVOEVAL be extended to test other aspects of LLM coding abilities, such as robustness to adversarial inputs or generalization to real-world programming tasks

EVOEVAL can be extended to test other aspects of LLM coding abilities by incorporating additional benchmarks that focus on different dimensions of coding proficiency. For robustness to adversarial inputs, EVOEVAL can introduce challenges where the LLMs are required to handle inputs that are intentionally crafted to deceive or mislead the model. This can include inputs with subtle changes or noise that may lead to incorrect outputs. By evaluating how well LLMs perform under such adversarial conditions, their robustness and resilience can be assessed.
To test generalization to real-world programming tasks, EVOEVAL can introduce problems that mimic real-world scenarios and challenges faced by developers. These problems can involve integrating multiple programming concepts, dealing with messy or incomplete data, or requiring the LLM to understand and implement complex algorithms. By designing benchmarks that closely resemble actual programming tasks, the ability of LLMs to generalize their learning to practical applications can be evaluated.

What are the potential biases or limitations in the way EVOEVAL problems are generated, and how can they be addressed

Potential biases or limitations in the way EVOEVAL problems are generated include the reliance on the initial seed problems from HUMANEVAL, which may introduce biases based on the selection of those problems. To address this, a more diverse set of seed problems can be used to ensure a broader representation of coding challenges. Additionally, the targeted transformation prompts used to evolve the problems may inadvertently introduce biases based on the specific types of transformations applied. To mitigate this, a wider range of transformation prompts can be developed to cover various aspects of coding abilities.
Another limitation could be the manual refinement process, which may introduce human biases or errors. Implementing automated checks and validations during the refinement stage can help reduce these biases and ensure consistency in problem generation. Furthermore, the evaluation metrics used in EVOEVAL may not capture all aspects of LLM coding abilities, leading to potential gaps in the assessment. Including a diverse set of evaluation metrics that cover different dimensions of coding proficiency can help address this limitation.

How can the insights from EVOEVAL be used to guide the development of more effective training approaches for improving LLM program synthesis capabilities

The insights from EVOEVAL can guide the development of more effective training approaches for improving LLM program synthesis capabilities by highlighting specific areas where LLMs struggle or excel. For example, the observation that LLMs struggle with combining multiple programming concepts in the COMBINE problems can inform the design of training datasets that focus on enhancing compositional generalization skills. By incorporating more diverse and complex problems that require the integration of different coding concepts, LLMs can be trained to better handle such challenges.
Additionally, the findings from EVOEVAL regarding the sensitivity of instruction-following LLMs to subtle changes in problem descriptions can inform the development of training strategies that focus on enhancing adaptability and robustness to variations in input specifications. By exposing LLMs to a wide range of problem variations during training, they can learn to generalize better and perform more consistently across different problem types.
Overall, the insights from EVOEVAL can be used to tailor training approaches that target specific weaknesses identified in LLMs, ultimately leading to improved program synthesis capabilities in real-world applications.