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.
- alphaXiv
- arXiv
- CatalyzeX
- CORE Recommender
- DagsHub
- Gotit.pub
- Hugging Face
- IHDec
- Jensen-Shannon divergence
- large-language models
- Nicole Geumheon Liu
- ScienceCast
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