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New REPOCOD benchmark reveals LLMs struggle with real-world coding tasks

By PulseAugur Editorial · [1 sources] ·

A new benchmark called REPOCOD has been developed to evaluate the capabilities of large language models (LLMs) in real-world coding tasks. Existing benchmarks like CoderEval and HumanEval are criticized for focusing on short completions or limited-scale projects, failing to represent the complexity of actual software development. REPOCOD includes 980 whole-function generation tasks from 11 popular projects, with over half requiring repository-level context and featuring developer-written test cases. Evaluations of ten LLMs on REPOCOD showed that none achieved over 30% pass@1 accuracy, highlighting the need for more advanced LLMs to assist developers in complex coding scenarios. AI

IMPACT Highlights the gap between LLM performance on synthetic benchmarks and real-world coding complexity, indicating a need for more advanced models for practical software development.

RANK_REASON The cluster is about a new academic paper introducing a novel benchmark for evaluating LLMs in coding tasks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New REPOCOD benchmark reveals LLMs struggle with real-world coding tasks

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The cluster is about a new academic paper introducing a novel benchmark for evaluating LLMs in coding tasks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Shanchao Liang, Yiran Hu, Nan Jiang, Lin Tan ·

    Can Language Models Replace Programmers for Coding? REPOCOD Says 'Not Yet'

    arXiv:2410.21647v5 Announce Type: replace-cross Abstract: Recently, a number of repository-level code generation benchmarks-such as CoderEval, DevEval, RepoEval, RepoBench, and LongCodeArena-have emerged to evaluate the capabilities of large language models (LLMs) beyond standalo…

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