Sign up to save your podcasts
Or
Share “Reducing LLM deception at scale with self-other overlap fine-tuning” by Marc Carauleanu, Diogo de Lucena, Gunnar_Zarncke, Judd Rosenblatt, Mike Vaiana, Cameron Berg
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
“Reducing LLM deception at scale with self-other overlap fine-tuning” by Marc Carauleanu, Diogo de Lucena, Gunnar_Zarncke, Judd Rosenblatt, Mike Vaiana, Cameron Berg
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.
Summary
In 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) Conclusion
The original text contained 6 footnotes which were omitted from this narration.
---
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:
Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
View all episodes
By LessWrongThis research was conducted at AE Studio and supported by the AI Safety Grants programme administered by Foresight Institute with additional support from AE Studio.
Summary
In 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) Conclusion
The original text contained 6 footnotes which were omitted from this narration.
---
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:
Apple Podcasts and Spotify do not show images in the episode description. Try Pocket Casts, or another podcast app.
More shows like LessWrong (30+ Karma)
View all
Making Sense with Sam Harris
26,358 Listeners
Conversations with Tyler
2,397 Listeners
The Peter Attia Drive
7,818 Listeners
Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas
4,111 Listeners
ManifoldOne
87 Listeners
Your Undivided Attention
1,455 Listeners
All-In with Chamath, Jason, Sacks & Friedberg
8,768 Listeners
Machine Learning Street Talk (MLST)
90 Listeners
Dwarkesh Podcast
354 Listeners
Hard Fork
5,356 Listeners
The Ezra Klein Show
15,019 Listeners
Moonshots with Peter Diamandis
463 Listeners
No Priors: Artificial Intelligence | Technology | Startups
128 Listeners
Latent Space: The AI Engineer Podcast
65 Listeners
BG2Pod with Brad Gerstner and Bill Gurley
432 Listeners