New ForeSight framework predicts LLM harmful content from early signals

Researchers have developed ForeSight, a new framework designed to predict harmful content generation in large language models (LLMs) at an earlier stage. This method distills weak and redundant early safety signals from the first-token hidden states into compact, layer-aware risk representations. Experiments on multiple safety benchmarks and target models indicate that ForeSight offers superior and efficient early-risk forecasting compared to existing methods that rely on surface tokens, output logits, or dense internal representations. AI

IMPACT Enhances early detection of harmful content in LLMs, potentially improving model safety and reliability.

RANK_REASON The cluster contains a research paper detailing a new framework for LLM safety. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• alphaXiv
• arXiv
• CatalyzeX
• Connected Papers
• CORE Recommender
• DagsHub
• ForeSight
• Gotit.pub
• Hugging Face
• large language models
• Litmaps
• ScienceCast
• scite Smart Citations

• paper
• safety

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