PECC: Problem Extraction and Coding Challenges

• URL: http://arxiv.org/abs/2404.18766v1
• Date: Mon, 29 Apr 2024 15:02:14 GMT
• Title: PECC: Problem Extraction and Coding Challenges
• Authors: Patrick Haller, Jonas Golde, Alan Akbik,
• Abstract summary: We introduce PECC, a novel benchmark derived from Advent Of Code (AoC) challenges and Project Euler. Unlike conventional benchmarks, PECC requires LLMs to interpret narrative-embedded problems, extract requirements, and generate code. Results show varying model performance between narrative and neutral problems, with specific challenges in the Euler math-based subset.
• Score: 3.287942619833188
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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