Summary of Stress-testing Capability Elicitation with Password-locked Models, by Ryan Greenblatt et al.
Stress-Testing Capability Elicitation With Password-Locked Models
by Ryan Greenblatt, Fabien Roger, Dmitrii Krasheninnikov, David Krueger
First submitted to arxiv on: 29 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 paper investigates the conditions under which fine-tuning-based elicitation methods can successfully elicit the full capabilities of large language models (LLMs). To achieve this, the authors introduce password-locked models that deliberately hide certain capabilities unless a specific password is present in the prompt. The researchers test whether these hidden capabilities can be elicited without using the password, demonstrating that a few high-quality demonstrations are often sufficient to fully elicit the capabilities. Surprisingly, fine-tuning can also unlock other hidden capabilities using the same or different passwords. Furthermore, reinforcement learning approaches can still elicit capabilities even when only evaluations and not demonstrations are available. The findings suggest that fine-tuning is an effective method for eliciting hidden LLM capabilities, but may be unreliable in situations where high-quality demonstrations are scarce. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps scientists figure out how to safely test large language models (LLMs). Currently, simple ways of asking questions often don’t bring out the full potential of these models. To solve this problem, researchers created special LLMs that can only do certain things if a specific “password” is included in the question. The study shows that giving these LLMs a few good examples is usually enough to get them to show their full capabilities. What’s more surprising is that once you’ve unlocked one hidden capability, you might be able to unlock others using the same or different passwords. This research helps us understand how to safely test and learn from LLMs. |
Keywords
» Artificial intelligence » Fine tuning » Prompt » Reinforcement learning
