Summary of Dynamic Ensemble Reasoning For Llm Experts, by Jinwu Hu et al.
Dynamic Ensemble Reasoning for LLM Experts
by Jinwu Hu, Yufeng Wang, Shuhai Zhang, Kai Zhou, Guohao Chen, Yu Hu, Bin Xiao, Mingkui Tan
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 Dynamic Ensemble Reasoning paradigm called DER, which integrates the strengths of multiple Large Language Model (LLM) experts conditioned on dynamic inputs. To achieve this, the authors model the LLM ensemble reasoning problem as a Markov Decision Process (MDP), where an agent selects knowledge from one LLM and passes it to another based on the input questions. The goal is to achieve high performance with minimal computational resources. The authors also develop a Knowledge Transfer Prompt (KTP) to transfer expert knowledge effectively between LLMs. Experimental results show that DER uses fewer resources to achieve better performance compared to state-of-the-art baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers work better together by combining the strengths of many language models. It’s like having a team of experts working together to answer questions. The authors came up with a new way to make this happen, using something called a Markov Decision Process. This process lets the computer figure out which expert to ask for help and when. The goal is to get good answers quickly, without using too much energy or resources. To make it even better, the authors created a special prompt that helps the experts share their knowledge with each other. By working together like this, computers can answer questions more accurately and efficiently. |
Keywords
» Artificial intelligence » Large language model » Prompt
