Summary of Mixture Of Insightful Experts (mote): the Synergy Of Thought Chains and Expert Mixtures in Self-alignment, by Zhili Liu et al.
Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment
by Zhili Liu, Yunhao Gou, Kai Chen, Lanqing Hong, Jiahui Gao, Fei Mi, Yu Zhang, Zhenguo Li, Xin Jiang, Qun Liu, James T. Kwok
First submitted to arxiv on: 1 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed Mixture of insighTful Experts (MoTE) framework combines reasoning chains and expert mixtures to improve the alignment of large language models with human values. MoTE employs a structured reasoning chain comprising four stages: Question Analysis, Answer Guidance, Safe Answer, and Safety Checking, which enhances safety through multi-step reasoning. The architecture adopts a multi-LoRA framework with step-level routing, eliminating balance losses and supporting adaptive inference lengths. Experimental results show that MoTE improves model safety, jailbreak resistance, and over-refusal capabilities, achieving performance comparable to OpenAI’s o1 model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can be very good at doing things we want them to do, like answering questions or generating text. But they’re not always great at understanding what’s right and wrong. This paper proposes a new way to make these models safer by giving them more steps to think through their answers. It also makes it easier for the model to figure out when it’s making a mistake. The new approach is called MoTE, which stands for Mixture of insighTful Experts. It works by breaking down the thinking process into smaller parts and then combining those parts in different ways. This helps the model make better choices and avoid mistakes. |
Keywords
» Artificial intelligence » Alignment » Inference » Lora
