Understanding LLM Performance Degradation in Multi-Instance Processing: The Roles of Instance Count and Context Length
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866918457570754560 |
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| author | Chen, Jingxuan Pilehvar, Mohammad Taher Camacho-Collados, Jose |
| author_facet | Chen, Jingxuan Pilehvar, Mohammad Taher Camacho-Collados, Jose |
| contents | Users often rely on Large Language Models (LLMs) for processing multiple documents or performing analysis over a number of instances. For example, analysing the overall sentiment of a number of movie reviews requires an LLM to process the sentiment of each review individually in order to provide a final aggregated answer. While LLM performance on such individual tasks is generally high, there has been little research on how LLMs perform when dealing with multi-instance inputs. In this paper, we perform a comprehensive evaluation of the multi-instance processing (MIP) ability of LLMs for tasks in which they excel individually. The results show that all LLMs follow a pattern of slight performance degradation for small numbers of instances (approximately 20-100), followed by a performance collapse on larger instance counts. Crucially, our analysis shows that while context length is associated with this degradation, the number of instances has a stronger effect on the final results. This finding suggests that when optimising LLM performance for MIP, attention should be paid to both context length and, in particular, instance count. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_22608 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Understanding LLM Performance Degradation in Multi-Instance Processing: The Roles of Instance Count and Context Length Chen, Jingxuan Pilehvar, Mohammad Taher Camacho-Collados, Jose Artificial Intelligence Computation and Language Users often rely on Large Language Models (LLMs) for processing multiple documents or performing analysis over a number of instances. For example, analysing the overall sentiment of a number of movie reviews requires an LLM to process the sentiment of each review individually in order to provide a final aggregated answer. While LLM performance on such individual tasks is generally high, there has been little research on how LLMs perform when dealing with multi-instance inputs. In this paper, we perform a comprehensive evaluation of the multi-instance processing (MIP) ability of LLMs for tasks in which they excel individually. The results show that all LLMs follow a pattern of slight performance degradation for small numbers of instances (approximately 20-100), followed by a performance collapse on larger instance counts. Crucially, our analysis shows that while context length is associated with this degradation, the number of instances has a stronger effect on the final results. This finding suggests that when optimising LLM performance for MIP, attention should be paid to both context length and, in particular, instance count. |
| title | Understanding LLM Performance Degradation in Multi-Instance Processing: The Roles of Instance Count and Context Length |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2603.22608 |