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Language models struggle with long inputs due to attention competition, not length

By PulseAugur Editorial · [2 sources] ·

A new study reveals that language models struggle with long inputs not due to sequence length itself, but due to "competition" for attention within the model. Researchers found that neutral filler text had minimal impact on accuracy for models like Qwen2.5-1.5B up to 32K tokens. However, introducing rival statements or evidence-formatted text significantly degraded performance, with attention mass on the evidence correlating strongly with accuracy loss. This suggests that context rot in retrieval tasks is primarily an attention competition problem, not a simple length limitation. AI

IMPACT This research suggests that improving long-context performance may require architectural changes to better manage attention competition, rather than simply increasing context window sizes.

RANK_REASON Research paper detailing findings on language model behavior.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Language models struggle with long inputs due to attention competition, not length

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Research paper detailing findings on language model behavior.
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Length Is Cheap, Competition Is Not: Attention Predicts Context Rot in Retrieval

    Why do language models fail on long inputs? The common answer is length. We show that, for retrieval, length is cheap and competition is not. On five open models (0.5B to 3B parameters, four families), neutral filler costs nothing: accuracy stays at 0.97 to 1.00, up to 32K tokens…

  2. Hugging Face Daily Papers TIER_1 English(EN) ·

    Length Is Cheap, Competition Is Not: Attention Predicts Context Rot in Retrieval

    In retrieval tasks, language model failure on long inputs stems from attention competition rather than sequence length, as rival evidence statements sharply reduce accuracy while neutral filler does not.

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