Fine-tuning an LLM on harmful data can make it broadly misaligned. But models are rarely trained on harmful data in practice.
We found a quieter route.
Training a model on facts it believes to be false, with no harmful content at all, misaligns it too. 🧵
Fine-tuning an LLM on harmful data can make it broadly misaligned. But models are rarely trained on harmful data in practice.
We found a quieter route.
Training a model on facts it believes to be false, with no harmful content at all, misaligns it too. 🧵
To get a good idea of this project, I recommend reading these papers:
1. Faithfulness as information flow - https://t.co/YchOrKaaU1
2. Measuring Faithfulness in CoT Reasoning - https://t.co/CuLjkBE0r0
3. Reasoning models don't say what they think - https://t.co/ANMKAghFGV
Hey all! I'm mentoring a project in SPAR on Chain-of-Thought Faithfulness (https://t.co/tAe2yAgWUH) this cohort. Please consider applying to my project if you are interested in working on CoT monitoring, interpretability and evaluation.
The best part is that you do not have to start from scratch. I have run initial validation experiments and the results support my hypothesis very well. You will work on expanding the project to different models and various scenarios and will get to learn about CoT monitoring.
@adithya_s_k I'm using a similar method to go from ideation to results using AI only but didn't achieve much success. Your insights are really helpful, will use them and see how it goes.