LiveCodeBench: Comprehensive Evaluation of Large Language Models for Code

Concetti Chiave

The author proposes LiveCodeBench as a comprehensive and contamination-free benchmark to evaluate Large Language Models (LLMs) for code. The approach includes live updates, holistic evaluations, and diverse problem sets to address shortcomings in existing benchmarks.

Sintesi

LiveCodeBench introduces a new benchmark for evaluating LLMs applied to code-related applications. It addresses issues of contamination, overfitting, and limited evaluation scenarios present in current benchmarks. The benchmark includes scenarios like self-repair, code execution, and test output prediction to provide a more comprehensive assessment of LLM capabilities.
Large language models applied to coding tasks have seen significant advancements, but evaluation benchmarks have lagged behind. LiveCodeBench aims to fill this gap by providing a continuously updated platform with diverse problem sets sourced from reputable coding competition websites.
The benchmark focuses on preventing data contamination by using live updates and ensuring model evaluations are based on unseen problems. It also emphasizes the importance of holistic evaluations that go beyond natural language-to-code tasks.
By evaluating 29 models across various sizes and families, LiveCodeBench reveals insights into model performances across different scenarios. The findings highlight the significance of fine-tuning datasets, model sizes, and closed-access models in achieving better performance in code generation tasks.
Overall, LiveCodeBench serves as a valuable tool for advancing the understanding of current code LLMs and guiding future research in this domain.

Statistiche

Currently hosts over 400 instances from problems released between May and February.
DeepSeek models were released in Sep 2023.
DS-Ins-33B achieves 23.4 Pass@1 on LiveCodeBench.
GPT-4-Turbo outperforms GPT-4 in self-repair scenario.
DS models significantly outperform CodeLLaMa and StarCoder2 base models.
Fine-tuned DeepSeek models lead in performance across most scenarios.
Closed (API access) models generally outperform open access models.
Closed-access models consistently perform better than open-access instruction-tuned variants.

Citazioni

"Models cluster into two groups - those performing well on both HumanEval+ and LiveCodeBench vs. those excelling only on HumanEval+" - Author
"Fine-tuning improves performance on both HumanEval+ and LiveCodeBench for the code generation scenario" - Researcher

Approfondimenti chiave tratti da

LiveCodeBench

by Naman Jain,K... alle arxiv.org 03-14-2024

https://arxiv.org/pdf/2403.07974.pdf

Domande più approfondite

How can LiveCodeBench be adapted to include evaluation scenarios beyond competition programming?

LiveCodeBench can be expanded to include evaluation scenarios beyond competition programming by diversifying the sources of problems. Currently, the benchmark focuses on problems sourced from LeetCode, AtCoder, and CodeForces. To incorporate a broader range of evaluation scenarios, LiveCodeBench could consider including problems from other coding platforms such as HackerRank, CodeChef, or TopCoder. These platforms offer different types of coding challenges that cover various aspects of software development.
Additionally, LiveCodeBench could introduce new problem categories that reflect real-world coding tasks encountered in software engineering projects. For example, scenarios like web development (HTML/CSS/JavaScript), data manipulation (SQL), algorithm design (graph algorithms), or system design questions could be included to provide a more comprehensive assessment of LLMs for code across different domains.
Furthermore, incorporating industry-specific challenges or open-ended coding tasks commonly found in software development interviews would enhance the benchmark's relevance to practical applications outside competitive programming. By expanding the scope of evaluation scenarios and problem sources, LiveCodeBench can offer a more holistic assessment of LLM capabilities for diverse coding tasks.

How might the inclusion of other programming languages impact the effectiveness of LiveCodeBench?

The inclusion of other programming languages in LiveCodeBench would significantly impact its effectiveness by providing a more comprehensive evaluation of LLMs across multiple language domains. Currently focusing on Python limits the generalizability and applicability of evaluations conducted within this specific language context.
By incorporating additional languages such as Java, C++, JavaScript, or Ruby into LiveCodeBench's evaluation scenarios, researchers and developers can assess how well LLMs generalize their code generation capabilities across different syntaxes and paradigms. This expansion would enable a more robust understanding of model performance in diverse environments where multiple languages are used concurrently.
Moreover, evaluating LLMs on various programming languages allows for insights into language-specific nuances and challenges faced during code generation tasks. Different languages have unique features and conventions that impact how code is written and understood by both humans and machines. By testing models on multiple languages within LiveCodeBench, researchers can gain valuable insights into cross-language transfer learning abilities and identify areas for improvement in multi-lingual code generation tasks.
In summary,
the inclusion
of other
programming
languages
in
Livecodebench will enhance its versatility,
provide deeper insights into model performance across diverse linguistic contexts,
and facilitate research advancements in multi-lingual code generation capabilities.

What strategies can be implemented to reduce noise due to limited evaluation set size in newer models?

To mitigate noise resulting from limited evaluation set sizes when assessing newer models on Livecodebench:
1.
Strategic Data Augmentation: Implement data augmentation techniques such as text paraphrasing,
synonym replacement,
or sentence restructuring to artificially expand the dataset available for evaluations.
This approach helps create variations within existing problems without compromising quality.
2.
Transfer Learning: Utilize pre-trained models with domain knowledge or fine-tuned weights from related benchmarks
to bootstrap training processes for newer models with smaller datasets.
This strategy leverages existing information effectively while reducing reliance solely on limited data points.
3.
Active Learning Strategies: Employ active learning methodologies where models actively select which instances
from an unlabeled pool should be labeled next based on uncertainty sampling or confidence scores generated during predictions.
This iterative process optimizes labeling efforts towards maximizing information gain while minimizing noise introduced by small sample sizes.
These strategies collectively aim at enhancing model robustness against noisy signals stemming from restricted dataset sizes typically observed with newer models undergoing evaluations.