This research was conducted at AE Studio and supported by the AI Safety Grants programme administered by Foresight Institute with additional support from AE Studio.** SummaryIn this post, we summarise the main experimental results from our new paper, "Towards Safe and Honest AI Agents with Neural Self-Other Overlap", which we presented orally at the Safe Generative AI Workshop at NeurIPS 2024. This is a follow-up to our post Self-Other Overlap: A Neglected Approach to AI Alignment, which introduced the method last July.Our results show that the Self-Other Overlap (SOO) fine-tuning drastically[1] reduces deceptive responses in language models (LLMs), with minimal impact on general performance, across the scenarios we evaluated. LLM Experimental Setup**We adapted a text scenario from Hagendorff designed to test LLM deception capabilities. In this scenario, the LLM must choose to recommend a room to a would-be burglar, where one room holds an expensive item [...] ---Outline:(00:19) Summary(00:57) LLM Experimental Setup(04:05) LLM Experimental Results(05:04) Impact on capabilities(05:46) Generalisation experiments(08:33) Example Outputs(09:04) ConclusionThe original text contained 6 footnotes which were omitted from this narration. The original text contained 2 images which were described by AI. --- First published: March 13th, 2025 Source: https://www.lesswrong.com/posts/jtqcsARGtmgogdcLT/reducing-llm-deception-at-scale-with-self-other-overlap-fine) --- Narrated by TYPE III AUDIO). ---Images from the article:)))))
