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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2412.07923 |
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| _version_ | 1866916650619502592 |
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| author | Shaier, Sagi Sanz-Guerrero, Mario von der Wense, Katharina |
| author_facet | Shaier, Sagi Sanz-Guerrero, Mario von der Wense, Katharina |
| contents | This study investigates whether repeating questions within prompts influences the performance of large language models (LLMs). We hypothesize that reiterating a question within a single prompt might enhance the model's focus on key elements of the query. We evaluate five recent LLMs -- including GPT-4o-mini, DeepSeek-V3, and smaller open-source models -- on three reading comprehension datasets under different prompt settings, varying question repetition levels (1, 3, or 5 times per prompt). Our results demonstrate that question repetition can increase models' accuracy by up to $6\%$. However, across all models, settings, and datasets, we do not find the result statistically significant. These findings provide insights into prompt design and LLM behavior, suggesting that repetition alone does not significantly impact output quality. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_07923 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Asking Again and Again: Exploring LLM Robustness to Repeated Questions Shaier, Sagi Sanz-Guerrero, Mario von der Wense, Katharina Computation and Language This study investigates whether repeating questions within prompts influences the performance of large language models (LLMs). We hypothesize that reiterating a question within a single prompt might enhance the model's focus on key elements of the query. We evaluate five recent LLMs -- including GPT-4o-mini, DeepSeek-V3, and smaller open-source models -- on three reading comprehension datasets under different prompt settings, varying question repetition levels (1, 3, or 5 times per prompt). Our results demonstrate that question repetition can increase models' accuracy by up to $6\%$. However, across all models, settings, and datasets, we do not find the result statistically significant. These findings provide insights into prompt design and LLM behavior, suggesting that repetition alone does not significantly impact output quality. |
| title | Asking Again and Again: Exploring LLM Robustness to Repeated Questions |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.07923 |