The Nonlinear Library AF - The Bitter Lesson for AI Safety Research by Adam Khoja
Aug 2, 2024
06:33
Welcome to The Nonlinear Library, where we use Text-to-Speech software to convert the best writing from the Rationalist and EA communities into audio. This is: The Bitter Lesson for AI Safety Research, published by Adam Khoja on August 2, 2024 on The AI Alignment Forum.
Read the associated paper "Safetywashing: Do AI Safety Benchmarks
Actually Measure Safety Progress?": https://arxiv.org/abs/2407.21792
Focus on safety problems that aren't solved with scale.
Benchmarks are crucial in ML to operationalize the properties we want models to have (knowledge, reasoning, ethics, calibration, truthfulness, etc.). They act as a criterion to judge the quality of models and drive implicit competition between researchers. "For better or worse, benchmarks shape a field."
We performed the largest empirical meta-analysis to date of AI safety benchmarks on dozens of open language models. Around half of the benchmarks we examined had high correlation with upstream general capabilities.
Some safety properties improve with scale, while others do not. For the models we tested, benchmarks on human preference alignment, scalable oversight (e.g., QuALITY), truthfulness (TruthfulQA MC1 and TruthfulQA Gen), and static adversarial robustness were highly correlated with upstream general capabilities. Bias, dynamic adversarial robustness, and calibration when not measured with Brier scores had relatively low correlations.
Sycophancy and weaponization restriction (WMDP) had significant negative correlations with general capabilities.
Often, intuitive arguments from alignment theory are used to guide and prioritize deep learning research priorities. We find these arguments to be poorly predictive of these correlations and are ultimately counterproductive. In fact, in areas like adversarial robustness, some benchmarks basically measured upstream capabilities while others did not.
We argue instead that empirical measurement is necessary to determine which safety properties will be naturally achieved by more capable systems, and which safety problems will remain persistent.[1] Abstract arguments from genuinely smart people may be highly "thoughtful," but these arguments generally do not track deep learning phenomena, as deep learning is too often counterintuitive.
We provide several recommendations to the research community in light of our analysis:
Measure capabilities correlations when proposing new safety evaluations.
When creating safety benchmarks, aim to measure phenomena which are less correlated with capabilities. For example, if truthfulness entangles Q/A accuracy, honesty, and calibration - then just make a decorrelated benchmark that measures honesty or calibration.
In anticipation of capabilities progress, work on safety problems that are disentangled with capabilities and thus will likely persist in future models (e.g., GPT-5). The ideal is to find training techniques that cause as many safety properties as possible to be entangled with capabilities.
Ultimately, safety researchers should prioritize differential safety progress, and should attempt to develop a science of benchmarking that can effectively identify the most important research problems to improve safety relative to the default capabilities trajectory.
We're not claiming that safety properties and upstream general capabilities are orthogonal. Some are, some aren't. Safety properties are not a monolith. Weaponization risks increase as upstream general capabilities increase. Jailbreaking robustness isn't strongly correlated with upstream general capabilities.
However, if we can isolate less-correlated safety properties in AI systems which are distinct from greater intelligence, these are the research problems safety researchers should most aggressively pursue and allocate resources toward. The other model properties can be left to capabilities researchers.
This amounts to a "Bitter Lesson" argument for working on safety issues which are relatively uncorrelated (or negatively correlate...
Read the associated paper "Safetywashing: Do AI Safety Benchmarks
Actually Measure Safety Progress?": https://arxiv.org/abs/2407.21792
Focus on safety problems that aren't solved with scale.
Benchmarks are crucial in ML to operationalize the properties we want models to have (knowledge, reasoning, ethics, calibration, truthfulness, etc.). They act as a criterion to judge the quality of models and drive implicit competition between researchers. "For better or worse, benchmarks shape a field."
We performed the largest empirical meta-analysis to date of AI safety benchmarks on dozens of open language models. Around half of the benchmarks we examined had high correlation with upstream general capabilities.
Some safety properties improve with scale, while others do not. For the models we tested, benchmarks on human preference alignment, scalable oversight (e.g., QuALITY), truthfulness (TruthfulQA MC1 and TruthfulQA Gen), and static adversarial robustness were highly correlated with upstream general capabilities. Bias, dynamic adversarial robustness, and calibration when not measured with Brier scores had relatively low correlations.
Sycophancy and weaponization restriction (WMDP) had significant negative correlations with general capabilities.
Often, intuitive arguments from alignment theory are used to guide and prioritize deep learning research priorities. We find these arguments to be poorly predictive of these correlations and are ultimately counterproductive. In fact, in areas like adversarial robustness, some benchmarks basically measured upstream capabilities while others did not.
We argue instead that empirical measurement is necessary to determine which safety properties will be naturally achieved by more capable systems, and which safety problems will remain persistent.[1] Abstract arguments from genuinely smart people may be highly "thoughtful," but these arguments generally do not track deep learning phenomena, as deep learning is too often counterintuitive.
We provide several recommendations to the research community in light of our analysis:
Measure capabilities correlations when proposing new safety evaluations.
When creating safety benchmarks, aim to measure phenomena which are less correlated with capabilities. For example, if truthfulness entangles Q/A accuracy, honesty, and calibration - then just make a decorrelated benchmark that measures honesty or calibration.
In anticipation of capabilities progress, work on safety problems that are disentangled with capabilities and thus will likely persist in future models (e.g., GPT-5). The ideal is to find training techniques that cause as many safety properties as possible to be entangled with capabilities.
Ultimately, safety researchers should prioritize differential safety progress, and should attempt to develop a science of benchmarking that can effectively identify the most important research problems to improve safety relative to the default capabilities trajectory.
We're not claiming that safety properties and upstream general capabilities are orthogonal. Some are, some aren't. Safety properties are not a monolith. Weaponization risks increase as upstream general capabilities increase. Jailbreaking robustness isn't strongly correlated with upstream general capabilities.
However, if we can isolate less-correlated safety properties in AI systems which are distinct from greater intelligence, these are the research problems safety researchers should most aggressively pursue and allocate resources toward. The other model properties can be left to capabilities researchers.
This amounts to a "Bitter Lesson" argument for working on safety issues which are relatively uncorrelated (or negatively correlate...