Understanding LLM Performance Degradation in Multi-Instance Processing: The Roles of Instance Count and Context Length
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 perfo...
| Authors | Jingxuan Chen, Mohammad Taher Pilehvar, Jose Camacho-Collados |
|---|---|
| Primary category | cs.AI |
| Published | 23 March 2026 |
| Last updated at source | 20 April 2026 |
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