Summary of Chain-of-specificity: An Iteratively Refining Method For Eliciting Knowledge From Large Language Models, by Kaiwen Wei et al.
Chain-of-Specificity: An Iteratively Refining Method for Eliciting Knowledge from Large Language Models
by Kaiwen Wei, Jingyuan Zhang, Hongzhi Zhang, Fuzheng Zhang, Di Zhang, Li Jin, Yue Yu
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach called Chain-of-Specificity (CoS) to improve the generative capabilities of Large Language Models (LLMs). Existing methods struggle with adhering to specific constraints, often producing generic or unsatisfactory responses. CoS iteratively emphasizes specific constraints in input instructions, unlocks knowledge within LLMs, and refines responses. Experimental results on complex datasets show that CoS outperforms existing methods in enhancing generated content for specificity. As the number of specific constraints increases, other baselines falter while CoS remains effective. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers generate better answers by following rules. Sometimes, big language models get confused and give generic responses instead of what’s needed. To fix this, researchers created a new method called Chain-of-Specificity (CoS). It makes the model pay attention to specific instructions and improve its answers. The results show that CoS works well even when there are many rules to follow. |
Keywords
* Artificial intelligence * Attention
