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New IHDec method secures LLM instruction hierarchies without fine-tuning · 2 sources tracked

By PulseAugur Editorial · [2 sources] ·

Researchers have developed IHDec, a novel method to address instruction hierarchy failures in large language models (LLMs) during multi-turn conversations. Unlike previous solutions that require costly fine-tuning, IHDec operates without training by using Jensen-Shannon Divergence to detect and correct violations where subordinate instructions override superior ones. Evaluations show IHDec surpasses training-based methods in handling multi-turn conflicts while maintaining response quality and enhancing safety against adversarial attacks. AI

IMPACT Enhances LLM reliability in complex, multi-turn instruction scenarios and improves safety against adversarial inputs.

RANK_REASON Research paper detailing a new method for LLMs.

Read on arXiv cs.CL →

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

New IHDec method secures LLM instruction hierarchies without fine-tuning · 2 sources tracked

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Research paper detailing a new method for LLMs.
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COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Nicole Geumheon Liu, Haeun Jang, Yonghyun Jun, Hwanhee Lee ·

    IHDec: Divergence-Steered Contrastive Decoding for Securing Multi-Turn Instruction Hierarchies

    arXiv:2606.29960v1 Announce Type: new Abstract: Large Language Models (LLMs) often fail to maintain instruction hierarchies (IH) when processing multi-source inputs with varying role-level priorities, paradoxically adhering to lower-priority directives during conflicts. While exi…

  2. arXiv cs.CL TIER_1 English(EN) · Hwanhee Lee ·

    IHDec: Divergence-Steered Contrastive Decoding for Securing Multi-Turn Instruction Hierarchies

    Large Language Models (LLMs) often fail to maintain instruction hierarchies (IH) when processing multi-source inputs with varying role-level priorities, paradoxically adhering to lower-priority directives during conflicts. While existing defenses mitigate this issue, they are lar…

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