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LLMs fail word counting due to specific MLP block, not representation limits

By PulseAugur Editorial · [1 sources] ·

Researchers have identified a specific mechanism within large language models that causes them to fail at counting repeated words, despite correctly encoding the count in their internal representations. A multi-layer perceptron block, located around 85-93% of the network's depth, overwrites the accurate count with an incorrect one before it reaches the output. This phenomenon was observed consistently across different model sizes of Llama-3.2 and Qwen2.5, indicating a common architectural issue rather than a limitation in representation. AI

IMPACT Identifies a specific computational bottleneck in LLMs that could be targeted for improved reasoning capabilities.

RANK_REASON Academic paper detailing a specific finding about LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs fail word counting due to specific MLP block, not representation limits

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Academic paper detailing a specific finding about LLM behavior. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Sohan Venkatesh ·

    Repeated-Token Counting Reveals a Dissociation Between Representations and Outputs

    arXiv:2605.09239v2 Announce Type: replace Abstract: Large language models fail at counting how many times a word repeats in a list, even though they perform well on far harder reasoning tasks. These failures are commonly attributed to limitations in internal count tracking. We sh…

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