View From Above: A Framework for Evaluating Distribution Shifts in Model Behavior
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866909328252862464 |
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| author | Chopra, Tanush Li, Michael Haimes, Jacob |
| author_facet | Chopra, Tanush Li, Michael Haimes, Jacob |
| contents | When large language models (LLMs) are asked to perform certain tasks, how can we be sure that their learned representations align with reality? We propose a domain-agnostic framework for systematically evaluating distribution shifts in LLMs decision-making processes, where they are given control of mechanisms governed by pre-defined rules. While individual LLM actions may appear consistent with expected behavior, across a large number of trials, statistically significant distribution shifts can emerge. To test this, we construct a well-defined environment with known outcome logic: blackjack. In more than 1,000 trials, we uncover statistically significant evidence suggesting behavioral misalignment in the learned representations of LLM. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_00948 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | View From Above: A Framework for Evaluating Distribution Shifts in Model Behavior Chopra, Tanush Li, Michael Haimes, Jacob Computation and Language Artificial Intelligence Machine Learning When large language models (LLMs) are asked to perform certain tasks, how can we be sure that their learned representations align with reality? We propose a domain-agnostic framework for systematically evaluating distribution shifts in LLMs decision-making processes, where they are given control of mechanisms governed by pre-defined rules. While individual LLM actions may appear consistent with expected behavior, across a large number of trials, statistically significant distribution shifts can emerge. To test this, we construct a well-defined environment with known outcome logic: blackjack. In more than 1,000 trials, we uncover statistically significant evidence suggesting behavioral misalignment in the learned representations of LLM. |
| title | View From Above: A Framework for Evaluating Distribution Shifts in Model Behavior |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2407.00948 |