Summary of Building Decision Making Models Through Language Model Regime, by Yu Zhang et al.
Building Decision Making Models Through Language Model Regime
by Yu Zhang, Haoxiang Liu, Feijun Jiang, Weihua Luo, Kaifu Zhang
First submitted to arxiv on: 12 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 “Learning then Using” (LTU) approach leverages large language models (LLMs) to tackle decision-making problems. Unlike traditional methods like expert systems, planning algorithms, or reinforcement learning that often require retraining for each unique task, LLMs demonstrate remarkable generalization capabilities across various language tasks. The LTU method involves a two-stage process: the “learning” phase develops a robust foundational decision-making model by integrating diverse knowledge from multiple domains and contexts, followed by the “using” phase which refines this foundation model for specific scenarios. This approach outperforms traditional supervised learning regimes in decision-making capabilities and generalization, as demonstrated in e-commerce domains like advertising and search optimization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The LTU approach uses large language models (LLMs) to make decisions. It’s a new way of doing things! Instead of having special experts or computers that only know about one thing, this method lets the LLM learn lots of different things and then use that knowledge to make good decisions. It’s like having a super-smart friend who can help you figure out what to do in all sorts of situations. |
Keywords
» Artificial intelligence » Generalization » Optimization » Reinforcement learning » Supervised
