LLMs Fail Gracefully: Long Context Performance Degrades Even in Simple Tasks
2025-07-15
This research challenges the common assumption that large language models (LLMs) perform uniformly well on long-context tasks. By extending the Needle in a Haystack benchmark and introducing variables like semantic matching and distractors, researchers found that even under simplified conditions, model performance degrades as input length increases. This was confirmed across conversational question answering and a repeated word replication task, revealing limitations in LLM long-context capabilities and suggesting potential challenges in real-world applications.
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