Summary of Learning to Decode Collaboratively with Multiple Language Models, by Shannon Zejiang Shen et al.
Learning to Decode Collaboratively with Multiple Language Models
by Shannon Zejiang Shen, Hunter Lang, Bailin Wang, Yoon Kim, David Sontag
First submitted to arxiv on: 6 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 method to teach multiple large language models (LLMs) to collaborate by interleaving their generations at the token level. The model treats the decision of which LLM generates the next token as a latent variable, optimizing the marginal likelihood of a training set under this latent variable model. This allows each base LLM to learn when to generate itself and when to call on an “assistant” language model without direct supervision. Token-level collaboration during decoding enables a fusion of each model’s expertise tailored to the specific task at hand. The authors demonstrate the effectiveness of their collaborative decoding method on instruction-following, domain-specific QA, and reasoning tasks, showing that the joint system outperforms individual models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps language models work together better by mixing their ideas when writing text. It’s like having a team of experts working together to solve a problem! The model learns when to contribute its own ideas and when to ask for help from other “assistant” models. This collaboration is especially useful when the models need to solve problems that require specific knowledge, like understanding medical jargon or explaining complex science concepts. By working together, the language models can perform better than they do alone. |
Keywords
* Artificial intelligence * Language model * Likelihood * Token
