How robust are natural language autoencoders to initialization?

Natural language autoencoders are meant to take in an LLM's activation vector and describe in plain text what the model is thinking. However, its training data collection involves asking Claude to guess what a model might be thinking. How robust are NLAs to these guesses? We change Claude's guesses in various ways and measure the impact on the NLA's statements as well as on reconstruction accuracy. We show that Qwen2.5-7B NLAs have some robustness to irrelevant statements and prevailing sentimen...