💡How is the CoT mechanism aligned with the knowledge interaction subspace?
📊 CoT maintains similar CK alignment compared to standard prompting for all the datasets, and also reduces PK alignment.

💡 Can we find reasons for hallucinations based on PK-CK interactions?
📊 The gap between PK and CK is much higher for the examples with hallucinated spans than for the examples with no hallucinated spans across the sequence steps.

💡 How do individual PK and CK contributions change over the NLE generation steps for different knowledge interactions?
📊 During most of the NLE generations, the model slightly prioritizes PK.

🪛 We propose a novel rank-2 projection subspace that disentangles PK and CK contributions more accurately and use it for the first multi-step analysis of knowledge interactions across longer NLE sequences.

💡 Is a rank-1 projection subspace enough for disentangling PK and CK contributions in all types of knowledge interaction scenarios?
📊 Different knowledge interactions are poorly captured by the rank-1 projection subspace in LLM model parameter

📊 Key Takeaways:
3️⃣ Real & Fictional Bias Mitigation: Reduces both real-world stereotypes (e.g., “Italians are reckless drivers”) and fictional associations (e.g., “citizens of a fictional country have blue skin”), making it useful for both safety and interpretability research.

📊 Key Takeaways:
2️⃣ Strong Generalization: Works on unseen biases during token-based fine-tuning.

📊 Key Takeaways:
1️⃣ Consistent Bias Elicitation: BiasGym reliably surfaces biases for mechanistic analysis, enabling targeted debiasing without hurting downstream performance.

🚀 Excited to share our new preprint: BiasGym: Fantastic LLM Biases and How to Find (and Remove) Them

📄 Read the paper: arxiv.org/abs/2508.08855