Deprecated: The each() function is deprecated. This message will be suppressed on further calls in /home/zhenxiangba/zhenxiangba.com/public_html/phproxy-improved-master/index.php on line 456 Paper page - LiveCodeBench: Holistic and Contamination Free Evaluation of Large
Language Models for Code
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However, as new and improved LLMs are developed,\nexisting evaluation benchmarks (e.g., HumanEval, MBPP) are no longer sufficient\nfor assessing their capabilities. In this work, we propose LiveCodeBench, a\ncomprehensive and contamination-free evaluation of LLMs for code, which\ncontinuously collects new problems over time from contests across three\ncompetition platforms, namely LeetCode, AtCoder, and CodeForces. Notably, our\nbenchmark also focuses on a broader range of code related capabilities, such as\nself-repair, code execution, and test output prediction, beyond just code\ngeneration. Currently, LiveCodeBench hosts four hundred high-quality coding\nproblems that were published between May 2023 and February 2024. We have\nevaluated 9 base LLMs and 20 instruction-tuned LLMs on LiveCodeBench. We\npresent empirical findings on contamination, holistic performance comparisons,\npotential overfitting in existing benchmarks as well as individual model\ncomparisons. We will release all prompts and model completions for further\ncommunity analysis, along with a general toolkit for adding new scenarios and\nmodel","upvotes":5,"discussionId":"65f8b07a1780bc3711323fe5","ai_summary":"LiveCodeBench is a new evaluation benchmark for LLMs in code-related tasks, focusing on continuous problem collection and assessment of self-repair, code execution, and test output prediction.","ai_keywords":["Large Language Models (LLMs)","LiveCodeBench","code-related applications","HumanEval","MBPP","LeetCode","AtCoder","CodeForces","self-repair","code execution","test output prediction","empirical findings","contamination","holistic performance comparisons","overfitting","general toolkit"]},"canReadDatabase":false,"canManagePapers":false,"canSubmit":false,"hasHfLevelAccess":false,"upvoted":false,"upvoters":[{"_id":"63a7422854f1d0225b075bfc","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63a7422854f1d0225b075bfc/XGYAcDPZG5ZEsNBWG6guw.jpeg","isPro":true,"fullname":"lhl","user":"leonardlin","type":"user"},{"_id":"63814d392dd1f3e7bf59862f","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/63814d392dd1f3e7bf59862f/XCyKK3NvV-DbIEjR4tUue.jpeg","isPro":false,"fullname":"Charlie Cheng-Jie Ji","user":"CharlieJi","type":"user"},{"_id":"629fd12579726ce6f4c47b63","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/1654713461737-629fd12579726ce6f4c47b63.png","isPro":false,"fullname":"Rif Hutchings","user":"hutchingsa","type":"user"},{"_id":"696a25cb58714222e17eb822","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/ahH1sc8M1zQu44gagNtU2.png","isPro":false,"fullname":"Md Nasib","user":"Nasib2008","type":"user"},{"_id":"697870b4185288f9650ef72e","avatarUrl":"/avatars/6918d5538abe7422d78111c49e278815.svg","isPro":false,"fullname":"Jeorge Reyes","user":"angmakabagongkatipunero","type":"user"}],"acceptLanguages":["*"]}">
LiveCodeBench is a new evaluation benchmark for LLMs in code-related tasks, focusing on continuous problem collection and assessment of self-repair, code execution, and test output prediction.
AI-generated summary
Large Language Models (LLMs) applied to code-related applications have
emerged as a prominent field, attracting significant interest from both
academia and industry. However, as new and improved LLMs are developed,
existing evaluation benchmarks (e.g., HumanEval, MBPP) are no longer sufficient
for assessing their capabilities. In this work, we propose LiveCodeBench, a
comprehensive and contamination-free evaluation of LLMs for code, which
continuously collects new problems over time from contests across three
competition platforms, namely LeetCode, AtCoder, and CodeForces. Notably, our
benchmark also focuses on a broader range of code related capabilities, such as
self-repair, code execution, and test output prediction, beyond just code
generation. Currently, LiveCodeBench hosts four hundred high-quality coding
problems that were published between May 2023 and February 2024. We have
evaluated 9 base LLMs and 20 instruction-tuned LLMs on LiveCodeBench. We
present empirical findings on contamination, holistic performance comparisons,
potential overfitting in existing benchmarks as well as individual model
comparisons. We will release all prompts and model completions for further
community analysis, along with a general toolkit for adding new scenarios and
model