Understanding LLM Performance Degradation in Multi-Instance Processing: The Roles of Instance Count and Context Length

• URL: http://arxiv.org/abs/2603.22608v1
• Date: Mon, 23 Mar 2026 22:13:15 GMT
• Title: Understanding LLM Performance Degradation in Multi-Instance Processing: The Roles of Instance Count and Context Length
• Abstract summary: Large Language Models (LLMs) are used to process multiple documents or perform analysis over a number of instances.<n>In this paper, we evaluate the ability of LLMs for tasks in which they excel individually.<n>The results show that all LLMs follow a pattern of slight performance degradation for small numbers of instances, followed by a performance collapse on larger instance counts.
• Score: 15.27910256601728
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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