Instruction tuning unlocks incredible skills in LLMs, but at a cost: they become dangerously overconfident. You face a choice: a well-calibrated base model or a capable but unreliable instruct model. What if you didn't have to choose? What if you could navigate the trade-off? (1/8)

We created Approximate Likelihood Matching, a principled (and very effective) method for *cross-tokenizer distillation*! With ALM, you can create ensembles of models from different families, convert existing subword-level models to byte-level and a bunch more🧵

Two amazing papers from my students at #NeurIPS today: ⛓️💥 Switch the vocabulary and embeddings of your LLM tokenizer zero-shot on the fly (@bminixhofer.bsky.social) neurips.cc/virtual/2024... 🌊 Align your LLM gradient-free with spectral editing of activations (Yifu Qiu) neurips.cc/virtual/2024...