Summary of View From Above: a Framework For Evaluating Distribution Shifts in Model Behavior, by Tanush Chopra et al.
View From Above: A Framework for Evaluating Distribution Shifts in Model Behavior
by Tanush Chopra, Michael Li, Jacob Haimes
First submitted to arxiv on: 1 Jul 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 Large language models (LLMs) are increasingly used to make decisions, but how can we ensure that their learned representations align with reality? To address this issue, we propose a domain-agnostic framework for evaluating distribution shifts in LLM decision-making processes. We create a well-defined environment, such as blackjack, and conduct over 1,000 trials to test whether the learned representations of LLMs are consistent with expected behavior. Our results show statistically significant evidence suggesting behavioral misalignment in the learned representations of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re playing a game like blackjack where computers make decisions based on rules. How can we be sure that these computer-made decisions match what’s really happening? We developed a way to test this by using a simple game and having the computer play it many times. Our results show that the computer’s “learned” ideas about how the game should be played don’t always match up with what actually happens. |
