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Research reveals LLMs retain hidden concepts despite suppression

By PulseAugur Editorial · [2 sources] ·

A new research paper explores the effectiveness of instruction-based suppression in large language models, finding that while models can be trained to avoid expressing prohibited content, the underlying concepts remain recoverable from their internal representations. The study utilized representational probing, attention analysis, and behavioral semantic leakage experiments across various transformer models. Results indicate that prohibited concepts continue to influence attention routing and shape downstream generations even when lexical avoidance is successful, revealing a significant gap between behavioral and representational alignment in these models. AI

IMPACT Reveals a fundamental gap in LLM safety mechanisms, suggesting current suppression techniques may not fully mitigate risks associated with prohibited content.

RANK_REASON The cluster contains a single academic paper published on arXiv.

Read on arXiv cs.AI →

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

Research reveals LLMs retain hidden concepts despite suppression

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Rebecca Ramnauth, Brian Scassellati ·

    The Attentional White Bear Effect in Transformer Language Models

    arXiv:2605.28639v1 Announce Type: cross Abstract: Instruction-based suppression is widely used to prevent language models from generating prohibited content, yet it remains unclear whether suppression reduces internal representation or merely suppresses expression. We investigate…

  2. arXiv cs.AI TIER_1 English(EN) · Brian Scassellati ·

    The Attentional White Bear Effect in Transformer Language Models

    Instruction-based suppression is widely used to prevent language models from generating prohibited content, yet it remains unclear whether suppression reduces internal representation or merely suppresses expression. We investigate this question through representational probing, a…

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