Summary of Can Large Language Models Explore In-context?, by Akshay Krishnamurthy et al.
Can large language models explore in-context?
by Akshay Krishnamurthy, Keegan Harris, Dylan J. Foster, Cyril Zhang, Aleksandrs Slivkins
First submitted to arxiv on: 22 Mar 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 This research investigates the capabilities of Large Language Models (LLMs) in exploring and making decisions without training interventions. The study deploys existing LLMs as agents in simple multi-armed bandit environments, using various prompt designs to test their performance. The results show that only one configuration, GPT-4 with chain-of-thought reasoning and an externally summarized interaction history, exhibits satisfactory exploratory behavior. This finding suggests that external summarization may be crucial for obtaining desirable behavior from LLM-based decision making agents in complex settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are super smart computers that can understand and generate human-like text. Researchers want to know if these models can also make good decisions on their own, without needing more training. To find out, they used the models to play a game where they had to choose between different options. The results show that most of the models didn’t do very well unless they were given special help and instructions. This means that we might need to give these models more guidance or information if we want them to make good decisions in real-world situations. |
Keywords
* Artificial intelligence * Gpt * Prompt * Summarization
