Benchmarking Large Language Models with Integer Sequence Generation Tasks

• URL: http://arxiv.org/abs/2411.04372v1
• Date: Thu, 07 Nov 2024 02:05:43 GMT
• Title: Benchmarking Large Language Models with Integer Sequence Generation Tasks
• Authors: Daniel O'Malley, Manish Bhattarai, Javier Santos,
• Abstract summary: This paper presents a novel benchmark where the large language model (LLM) must write code that computes integer sequences from the Online Encyclopedia of Sequences (OEIS) Our benchmark reveals that the o1 series of models outperform other frontier models from OpenAI, Anthropic, Meta, and Google in accuracy and cheating rates across both easy and hard integer sequences.
• Score: 1.3108652488669736
• License:
• Abstract: This paper presents a novel benchmark where the large language model (LLM) must write code that computes integer sequences from the Online Encyclopedia of Integer Sequences (OEIS), a widely-used resource for mathematical sequences. The benchmark is designed to evaluate both the correctness of the generated code and its computational efficiency. Our benchmark reveals that the o1 series of models outperform other frontier models from OpenAI, Anthropic, Meta, and Google in accuracy and cheating rates across both easy and hard integer sequences. In order to ensure models do not exploit memorized sequence values, we introduce an automated cheating detection mechanism that flags the use of lookup tables and validated this automation against human cheating evaluations. This benchmark provides a meaningful challenge for current LLMs, offering insights into their mathematical reasoning and code writing capabilities, which can guide future research directions and model development in mathematical reasoning and code synthesis.

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