SortBench: Benchmarking LLMs based on their ability to sort lists
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912320809074688 |
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| author | Herbold, Steffen |
| author_facet | Herbold, Steffen |
| contents | Sorting is a tedious but simple task for human intelligence and can be solved fairly easily algorithmically. However, for Large Language Models (LLMs) this task is surprisingly hard, as some properties of sorting are among known weaknesses of LLMs: being faithful to the input data, logical comparisons between values, and strictly differentiating between syntax (used for sorting) and semantics (typically learned by embeddings). Within this paper, we describe the new SortBench benchmark for LLMs that comes with different difficulties and that can be easily scaled in terms of difficulty. We apply this benchmark to seven state-of-the-art LLMs, including current test-time reasoning models. Our results show that while the o3-mini model is very capable at sorting in general, even this can be fooled if strings are defined to mix syntactical and semantical aspects, e.g., by asking to sort numbers written-out as word. Furthermore, all models have problems with the faithfulness to the input of long lists, i.e., they drop items and add new ones. Our results also show that test-time reasoning has a tendency to overthink problems which leads to performance degradation. Finally, models without test-time reasoning like GPT-4o are not much worse than reasoning models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_08312 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SortBench: Benchmarking LLMs based on their ability to sort lists Herbold, Steffen Machine Learning Artificial Intelligence Sorting is a tedious but simple task for human intelligence and can be solved fairly easily algorithmically. However, for Large Language Models (LLMs) this task is surprisingly hard, as some properties of sorting are among known weaknesses of LLMs: being faithful to the input data, logical comparisons between values, and strictly differentiating between syntax (used for sorting) and semantics (typically learned by embeddings). Within this paper, we describe the new SortBench benchmark for LLMs that comes with different difficulties and that can be easily scaled in terms of difficulty. We apply this benchmark to seven state-of-the-art LLMs, including current test-time reasoning models. Our results show that while the o3-mini model is very capable at sorting in general, even this can be fooled if strings are defined to mix syntactical and semantical aspects, e.g., by asking to sort numbers written-out as word. Furthermore, all models have problems with the faithfulness to the input of long lists, i.e., they drop items and add new ones. Our results also show that test-time reasoning has a tendency to overthink problems which leads to performance degradation. Finally, models without test-time reasoning like GPT-4o are not much worse than reasoning models. |
| title | SortBench: Benchmarking LLMs based on their ability to sort lists |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2504.08312 |