{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pecc-problem-extraction-and-coding-challenges","title":"PECC: Problem Extraction and Coding Challenges","arxiv_id":"2404.18766","date":"2024-04-29","proceeding":null,"authors":["Patrick Haller","Jonas Golde","Alan Akbik"],"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.","url_abs":"https://arxiv.org/abs/2404.18766v1","url_pdf":"https://arxiv.org/pdf/2404.18766v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pecc-problem-extraction-and-coding-challenges","repo_url":"https://github.com/hallerpatrick/pecc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"code-generation","task_name":"Code Generation"},{"task_slug":"math","task_name":"Math"},{"task_slug":"text-generation","task_name":"Text Generation"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"cosine-annealing","method_name":"Cosine Annealing"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"gpt-3","method_name":"GPT-3"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-cosine-annealing","method_name":"Linear Warmup With Cosine Annealing"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"}],"datasets_introduced":[{"slug":"pecc","name":"PECC","full_name":"PECC: Problem Extraction and Coding Challenges"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/code-generation-on-pecc","task":"Code Generation","dataset":"PECC","model":"Claude 3 Haiku","rank_in_archive_order":1,"of":8,"metrics":{"Pass@3":"27.67"},"uses_additional_data":false},{"leaderboard":"/sota/code-generation-on-pecc","task":"Code Generation","dataset":"PECC","model":"GPT-3.5 Turbo","rank_in_archive_order":2,"of":8,"metrics":{"Pass@3":"23.75"},"uses_additional_data":false},{"leaderboard":"/sota/code-generation-on-pecc","task":"Code Generation","dataset":"PECC","model":"codechat-bison","rank_in_archive_order":3,"of":8,"metrics":{"Pass@3":"11.39"},"uses_additional_data":false},{"leaderboard":"/sota/code-generation-on-pecc","task":"Code Generation","dataset":"PECC","model":"chat-bison","rank_in_archive_order":4,"of":8,"metrics":{"Pass@3":"8.48"},"uses_additional_data":false},{"leaderboard":"/sota/code-generation-on-pecc","task":"Code Generation","dataset":"PECC","model":"Mixtral-8x7B-Instruct","rank_in_archive_order":5,"of":8,"metrics":{"Pass@3":"8.35"},"uses_additional_data":false},{"leaderboard":"/sota/code-generation-on-pecc","task":"Code Generation","dataset":"PECC","model":"Phi-3-mini-128k-instruct","rank_in_archive_order":6,"of":8,"metrics":{"Pass@3":"7.18"},"uses_additional_data":false},{"leaderboard":"/sota/code-generation-on-pecc","task":"Code Generation","dataset":"PECC","model":"WizardLM-2-7B","rank_in_archive_order":7,"of":8,"metrics":{"Pass@3":"3.72"},"uses_additional_data":false},{"leaderboard":"/sota/code-generation-on-pecc","task":"Code Generation","dataset":"PECC","model":"Llama-3-8B-Instruct","rank_in_archive_order":8,"of":8,"metrics":{"Pass@3":"3.1"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2404.18766","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}