Summary of Embedding-aligned Language Models, by Guy Tennenholtz et al.
Embedding-Aligned Language Models
by Guy Tennenholtz, Yinlam Chow, Chih-Wei Hsu, Lior Shani, Ethan Liang, Craig Boutilier
First submitted to arxiv on: 24 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Emerging Technologies (cs.ET); 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 The proposed novel approach trains large language models (LLMs) to adhere to objectives defined within a latent embedding space by leveraging reinforcement learning (RL), treating a pre-trained LLM as an environment. The EAGLE agent is trained to iteratively steer the LLM’s generation towards optimal regions of the latent embedding space, w.r.t. some predefined criterion. The effectiveness of the EAGLE agent is demonstrated using the MovieLens 25M and Amazon Review datasets to surface content gaps that satisfy latent user demand. Additionally, an optimal design of a state-dependent action set is used to improve EAGLE’s efficiency. This work paves the way for controlled and grounded text generation using LLMs, ensuring consistency with domain-specific knowledge and data representations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are getting better at generating text that sounds like humans wrote it. But sometimes this generated text doesn’t make sense or doesn’t match what we want to say. To solve this problem, researchers proposed a new way to train LLMs using reinforcement learning (RL). They treat the pre-trained LLM as an environment and use RL to guide the model to generate text that meets certain criteria. This approach is called EAGLE (Embedding-Aligned Guided Language). The authors tested EAGLE on two big datasets: MovieLens 25M and Amazon Review. They showed that EAGLE can find content gaps that users might be looking for, but aren’t explicitly saying so. |
Keywords
» Artificial intelligence » Embedding » Embedding space » Reinforcement learning » Text generation
