Summary of Can Foundation Models Actively Gather Information in Interactive Environments to Test Hypotheses?, by Nan Rosemary Ke et al.
Can foundation models actively gather information in interactive environments to test hypotheses?
by Nan Rosemary Ke, Danny P. Sawyer, Hubert Soyer, Martin Engelcke, David P Reichert, Drew A. Hudson, John Reid, Alexander Lerchner, Danilo Jimenez Rezende, Timothy P Lillicrap, Michael Mozer, Jane X Wang
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 Foundation models are typically evaluated on their problem-solving abilities, but a crucial aspect of this process – actively gathering information to test hypotheses – has not been thoroughly investigated. To address this gap, researchers introduced a framework that allows foundation models to determine factors influencing a hidden reward function by iteratively reasoning about gathered information and proposing next exploratory actions to maximize information gain. The framework was implemented in both text-based and embodied 3D environments, allowing for high-throughput parameter sweeps and addressing complexities of multi-modal interaction relevant to real-world applications. The study found that LLM’s information gathering capability is close to optimal when identifying a single rewarding feature, but suboptimal when identifying a conjunction of features. Performance was comparable in both text and 3D embodied environments, although imperfect visual object recognition reduced accuracy in the 3D case. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models are really good at solving problems, but they’re not great at figuring out what to do next to solve those problems. Scientists created a special way for these models to learn by asking themselves questions and trying different answers to see which one is right. They tested this idea in two different ways: one where the model had to read text and another where it had to use its eyes and hands. The results showed that the model was great at solving simple problems, but struggled when things got a bit more complicated. |
Keywords
» Artificial intelligence » Multi modal
