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This study evaluates six proprietary LLMs across 16 DSM-5 conditions using adversarial attacks, finding that safety safeguards are only reliable for suicide and self-harm, with failure rates up to 100% for other conditions like eating disorders and substance use disorder.
The author argues that switching from proprietary AI models to open models is now much less of a professional sacrifice, citing improving open model quality and Claude's ID verification as a catalyst, similar to the historical shift from Windows to Linux.
New benchmarks like DeepSWE reveal a significant performance gap between proprietary and open-source AI models, causing disappointment in the open-source community.
Miles Brundage comments on the lack of quantitative analysis on how distillation affects the capability gap between open-weight and proprietary AI models, referencing a claim by Epoch AI that open-weight models lag by four months.