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Words & Weights: Streamlining Multi-Turn Interactions via Co-Adaptation
arXiv – CS AI|Chenxing Wei, Hong Wang, Ying He, Zhongxiang Dai, Bo Jiang, F. Richard Yu, Yao Shu||1 views
🤖AI Summary
Researchers introduce ROSA2, a framework that improves Large Language Model interactions by simultaneously optimizing both prompts and model parameters during test-time adaptation. The approach outperformed baselines by 30% on mathematical tasks while reducing interaction turns by 40%.
Key Takeaways
- →ROSA2 addresses multi-turn LLM interaction failures through joint optimization of words (prompts) and weights (parameters).
- →The framework decomposes error signals to use textual gradients for intent clarification and parameter updates for capability enhancement.
- →Theoretical analysis proves co-adaptation reduces required parameter shifts for model convergence.
- →Empirical results show 30% performance improvement on MATH dataset with 40% fewer interaction turns.
- →The research demonstrates that semantic clarity acts as a pre-conditioner for more effective parameter updates.
#large-language-models#machine-learning#test-time-adaptation#ai-optimization#parameter-tuning#prompt-engineering#rosa2#multi-turn-interaction
Read Original →via arXiv – CS AI
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