PECC: Problem Extraction and Coding Challenges
<|reference_start|>PECC: Problem Extraction and Coding Challenges: Recent advancements in large language models (LLMs) have showcased their exceptional abilities across various tasks, such as code generation, problem-solving and reasoning. Existing benchmarks evaluate tasks in isolation, yet the extent to which LLMs can understand prose-style tasks, identify the underlying problems, and then generate appropriate code solutions is still unexplored. Addressing this gap, we introduce PECC, a novel benchmark derived from Advent Of Code (AoC) challenges and Project Euler, including 2396 problems. Unlike conventional benchmarks, PECC requires LLMs to interpret narrative-embedded problems, extract requirements, and generate executable code. A key feature of our dataset is the complexity added by natural language prompting in chat-based evaluations, mirroring real-world instruction ambiguities. Results show varying model performance between narrative and neutral problems, with specific challenges in the Euler math-based subset with GPT-3.5-Turbo passing 50% of the AoC challenges and only 8% on the Euler problems. By probing the limits of LLMs' capabilities, our benchmark provides a framework to monitor and assess the subsequent progress of LLMs as a universal problem solver.<|reference_end|>
@article{haller2024pecc:,
    title={PECC: Problem Extraction and Coding Challenges},
    author={Patrick Haller, Jonas Golde, Alan Akbik},
    journal={arXiv preprint arXiv:2404.18766},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.18766},
    primaryClass={cs.AI}
}