Artificial Intelligence● neutralImpact 6/10
When Attention Closes: How LLMs Lose the Thread in Multi-Turn Interaction
cs.AI updates on arXiv.org·
✦AI Analysis
A new study reveals that large language models (LLMs) struggle to maintain focus during multi-turn interactions, leading to a decline in performance. The research introduces the Goal Accessibility Ratio (GAR) as a diagnostic tool to measure attention and predict failure in goal-oriented tasks across different model architectures.
Key Topics
large language modelsGoal Accessibility RatioMistralresidual representations
Originally reported by cs.AI updates on arXiv.org. Read the full article ↗