This is a collection of AI safety related paper replications that I've done.
- Will AI Tell Lies to Save Sick Children? Litmus-Testing AI Values Prioritization with AIRISKDILEMMAS - This paper takes 16 values such as honesty, freedom, and truthfulness and compares the model's stated preferences in binary choice questions versus the model's revealed preferences when given a choice between two scenarios. I found that the discrepancy between the models' stated and revealed preferences was smaller for frontier models like GPT 5.4 and Opus 4.6, but there was still a noticeable gap. Every model I tested had a very similar revealed preference ordering. Additionally, I found that by using non-mechanistic interventions, the stated preferences were quite steerable but did not fully align with the model's revealed preferences. By contrast, the revealed preferences were very robust. My write up can be found here.
- Emotion Concepts and their Function in a Large Language Model - This paper takes emotionally valenced stories and uses them to construct a set of linear probes to measure a model's emotion. These probes' geometry was then analyzed to find information about the emotion vectors (such as that angry is similar to frustrated). The probes are also shown to track semantic information and have a causal impact on the model. The original paper was run on Sonnet 4.5, and I replicated it and showed the results are visible on Gemma-2-2B, a small model. My notebook can be found here.
- Refusal in Language Models Is Mediated by a Single Direction - This paper takes LLM prompts that were rejected for safety reasons and compares them to harmless prompts to find a single "refusal vector". This refusal vector is shown to have a causal impact on the model, causing it to refuse harmless requests or allow harmful ones. I replicated these findings on Gemma-2B and was able to reproduce all the key findings of the paper. My notebook can be found here.