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GPT-2 Small audit finds 'cryptographic keys' feature linked to task failure

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a novel audit pipeline to analyze the internal workings of the GPT-2 Small language model, specifically focusing on its performance on the Indirect Object Identification (IOI) task. The study identified 146 features within the model's activations that correlate with task failure, with one prominent feature, labeled 'cryptographic keys,' showing a strong association with errors when the prompt's object is 'the keys.' While this feature is a significant correlate, causal ablation experiments indicated it is not a sufficient cause for failure at this layer, highlighting the complexity of understanding model behavior. AI

IMPACT Provides a new, efficient methodology for understanding and debugging language model behavior, potentially leading to more interpretable and reliable AI systems.

RANK_REASON The cluster contains an academic paper detailing a novel audit pipeline for analyzing a language model's internal activations and identifying correlates of task failure.

Read on arXiv cs.LG →

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

GPT-2 Small audit finds 'cryptographic keys' feature linked to task failure

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The cluster contains an academic paper detailing a novel audit pipeline for analyzing a language model's internal activations and identifying correlates of task failure.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Mahdi Nasermoghadasi ·

    Reading Task Failure Off the Activations: A Sparse-Feature Audit of GPT-2 Small on Indirect Object Identification

    arXiv:2605.22719v1 Announce Type: new Abstract: We report a small, reproducible audit of which sparse-autoencoder (SAE) features of GPT-2 small fire differently on failed versus successful trials of the Indirect Object Identification (IOI) task. On 300 prompts, GPT-2 small reache…

  2. arXiv cs.LG TIER_1 English(EN) · Mahdi Nasermoghadasi ·

    Reading Task Failure Off the Activations: A Sparse-Feature Audit of GPT-2 Small on Indirect Object Identification

    We report a small, reproducible audit of which sparse-autoencoder (SAE) features of GPT-2 small fire differently on failed versus successful trials of the Indirect Object Identification (IOI) task. On 300 prompts, GPT-2 small reaches 79.7% accuracy; 146 of the 24,576 features in …

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