Frontier LLMs maintain accuracy with short contexts only
Original title: You don't need long context
Compaction shows minimal accuracy loss on long-horizon tasks, suggesting semantic state space models.
Later tokens in long contexts are more costly to generate. Frequent compaction across tasks shows frontier models sacrifice little accuracy when maintaining short contexts. Performance does not collapse on long-horizon software engineering even with at most one action per context. This leads to the concept of semantic state space models questioning the need for long-context language models.
- cost disparity up to 7x on frontier models
- minimal accuracy loss with compaction
- performance holds on long-horizon tasks
- one action per context sufficient
- outlines semantic state space models
- Organizations
- Hugging Face
- AI models
- frontier open weight models
From the original article
1 min read at huggingface.co
Later tokens are costlier to generate than earlier tokens. On frontier open weight models, the disparity in cost may rise to as much as 7x. Through frequent compaction, we show across a broad range of long-horizon and long-input tasks that frontier language models sacrifice little to no task accuracy when maintaining short contexts.
By Peter Belcak, Saurav Muralidharan, Jindong Jiang · huggingface.co
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