Summary of Duo-llm: a Framework For Studying Adaptive Computation in Large Language Models, by Keivan Alizadeh et al.
Duo-LLM: A Framework for Studying Adaptive Computation in Large Language Models
by Keivan Alizadeh, Iman Mirzadeh, Hooman Shahrokhi, Dmitry Belenko, Frank Sun, Minsik Cho, Mohammad Hossein Sekhavat, Moin Nabi, Mehrdad Farajtabar
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Large Language Models (LLMs) typically generate outputs token by token using a fixed compute budget, leading to inefficient resource utilization. Recent advancements in mixture of expert (MoE) models, speculative decoding, and early exit strategies leverage the insight that computational demands can vary significantly based on the complexity and nature of the input. To address this need, we propose a novel framework that integrates smaller auxiliary modules within each Feed-Forward Network layer of the LLM. This design enables dynamic routing of tokens based on task complexity: tokens can be processed by either the small or big modules at each layer, or even bypass certain layers entirely. We show that trained routers operate differently from oracles and often yield suboptimal solutions. Notably, activating a large module in just one layer outperforms models that use large modules across all layers, underscoring the gap between practical implementations of routing in MoE models and theoretical optima for adaptive computation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) have limitations when it comes to using resources efficiently. Researchers have found ways to make them better by using mixture of expert (MoE) models and other techniques. However, they still need to figure out how to route tokens in a way that makes sense. In this paper, we propose a new approach that lets the model adjust its processing power based on the task’s complexity. This can help it process information more efficiently. The results show that when the model is trained to make decisions about where to route tokens, it doesn’t always make the best choices. |
Keywords
» Artificial intelligence » Token
