Had a blast at CoLM! It really was as good as everyone says, congrats to the organizers 🎉
This week I’ll be in New York giving talks at NYU, Yale, and Cornell Tech.
If you’re around and want to chat about LLM behavior, safety, interpretability, or just say hi - DM me!

In Vienna for #ACL2025, and already had my first (vegan) Austrian sausage!

Now hungry for discussing:
– LLMs behavior
– Interpretability
– Biases & Hallucinations
– Why eval is so hard (but so fun)
Come say hi if that’s your vibe too!

🔄 Step 2: Cross-tuning.
We swap instruction datasets between models with different pretraining.
Result: Biases follow the pretrained model!

PCA clearly shows models group by pretraining base, not by instruction.
The bias “signature” stays intact, no matter the finetuning!

🎲 Step 1: Training randomness.
We finetune the same model 3× with different seeds.
Result: Some variation in bias scores, but behavior patterns stay stable compared to MMLU variance.
✅ Aggregating across seeds reveals consistent trends.

🧪 We introduce a two-step causal framework to disentangle the effects of:
- Pretraining
- Instruction tuning
- Training randomness

- 🍁 Bottom line - pretraining is the origin of bias. Finetuning? Just the messenger
#CausalInference #TrustworthyAI #NLP

🚨New paper alert🚨

🧠
Instruction-tuned LLMs show amplified cognitive biases — but are these new behaviors, or pretraining ghosts resurfacing?

Excited to share our new paper, accepted to CoLM 2025🎉!
See thread below 👇
#BiasInAI #LLMs #MachineLearning #NLProc