Summary of Moin: Mixture Of Introvert Experts to Upcycle An Llm, by Ajinkya Tejankar et al.
MoIN: Mixture of Introvert Experts to Upcycle an LLM
by Ajinkya Tejankar, KL Navaneet, Ujjawal Panchal, Kossar Pourahmadi, Hamed Pirsiavash
First submitted to arxiv on: 13 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 paper proposes an innovative way to improve large language models without requiring extensive pre-training. The idea is to divide the pre-training data into meaningful subsets and train experts on each subset, which are lightweight adapters added on top of a frozen base model. During inference, incoming queries are routed to the most relevant expert, which is then used for the forward pass. Unlike traditional Mixture of Experts (MoE) models, these “introvert” experts do not collaborate with other experts for a single query. This approach enables extreme parallelism during training and inference, allowing for efficient processing on multiple GPUs. The authors demonstrate the effectiveness of their method through a proof-of-concept implementation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper tries to make an old language model better without needing to train it from scratch. It does this by breaking the data into smaller groups that are related to each other and then training special helpers called “experts” on each group. These experts are like tiny models that can be added to a bigger, already-trained model. When you want to use the model for something new, you pick the expert that’s most relevant to what you’re doing and then use it with the bigger model. This way, multiple experts can work together at the same time, making it really fast. The authors showed that this idea works by testing it out. |
Keywords
» Artificial intelligence » Inference » Language model » Mixture of experts
