Competition-Level Problems are Effective LLM Evaluators

By Yiming Huang, Zheng-Wen Lin, Xiao Liu, Yeyun Gong, Shuai Lu, Fangyu Lei, Yaobo Liang, Yelong Shen, Chen Lin, Nan Duan and 1 more. 2023

38 citations recorded by the source corpus.

Large language models (LLMs) have demonstrated impressive reasoning capabilities, yet there is ongoing debate about these abilities and the potential data contamination problem recently. This paper aims to evaluate the reasoning capacities of LLMs, specifically in solving recent competition-level programming problems in Codeforces, which are expert-crafted and unique, requiring deep understanding and robust reasoning skills. We first provide a comprehensive evaluation of GPT-4's peiceived zero-shot performance on this task, considering various aspects such as problems' release time, difficulties, and types of errors encountered. Surprisingly, the peiceived performance of GPT-4 has experienced a cliff like decline in problems after September 2021 consistently across all the difficulties and types of problems, which shows the potential data contamination, as well as the challenges for any existing LLM to solve unseen complex reasoning problems. We further explore various approaches such as fine-tuning, Chain-of-Thought prompting and problem description simplification, unfortunately none of them is able to consistently mitigate the challenges. Through our work, we emphasis the importance of this excellent data source for assessing the genuine reasoning capabilities of LLMs, and foster the development of LLMs with stronger reasoning abilities and better generalization in the future.

Subfields: Artificial Intelligence, Large Language Models, Evaluation & Benchmarks

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Large Language Models

In Evaluation & Benchmarks

Citations in the corpus

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code (2024, background): “(Huang et al., 2023) also evaluate LLMs in a time-segmented manner.”
Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference (2024, background): “These questions can be obtained from annual exams or weekly online contests such as Codeforces (Li et al., 2022; Huang et al., 2023).”