With scale, LMs become more exciting but, at the same time, harder to analyze. We show that even with simple methods and a single GPU, you can do a lot! We analyze OPT models up to 66b and find that

• neurons inside LLMs can be:
• dead, i.e. never activate on a large dataset,
• n-gram detectors that explicitly remove information about current input token;
• positional, i.e. encode "where" regardless of "what" and question the key-value memory view of FFNs;
• with scale, models have more dead neurons and token detectors and are less focused on absolute position.

In SMT, model competences are modelled with distinct models. In NMT, the whole translation task is modelled with a single neural network. How and when does NMT get to learn all the competences? We show that

• during training, NMT undergoes three different stages:
• target-side language modeling,
• learning how to use source and approaching word-by-word translation,
• refining translations, visible by increasingly complex reorderings but not visible by e.g. BLEU;
• not only this is fun, but it can also help in practice! For example, in settings where data complexity matters, such as non-autoregressive NMT.

• NMT model components can learn to extract features which in SMT were modelled explicitly;
• for NMT, we can also look at how it balances the two different types of context: the source and the prefix;
• NMT training consists of the stages where it focuses on competences mirroring three core SMT components.

• models suffering from exposure bias are more prone to over-relying on target history (and hence to hallucinating) than the ones where the exposure bias is mitigated;
• models trained with more data rely on the source more and do it more confidently;
• the training process is non-monotonic with several distinct stages.

• we propose information-theoretic probing which measures minimum description length (MDL) of labels given representations;
• we show that MDL characterizes both probe quality and the amount of effort needed to achieve it;
• we explain how to easily measure MDL on top of standard probe-training pipelines;
• we show that results of MDL probes are more informative and stable than those of standard probes.

• LMs gradually forget past when forming predictions about future;
• for MLMs, the evolution proceeds in two stages of context encoding and token reconstruction;
• MT representations get refined with context, but less processing is happening.

• which phenomena cause context-agnostic translations to be inconsistent with each other
• how we create test sets addressing the most frequent phenomena
• about a novel set-up for context-aware NMT with a large amount of sentence-level data and much less of document-level data
• about a new model for this set-up (Context-Aware Decoder, aka CADec) - a two-pass MT model which first produces a draft translation of the current sentence, then corrects it using context.

• how we evaluate the importance of attention heads in Transformer
• which functions the most important encoder heads perform
• how we prune the vast majority of attention heads in Transformer without seriously affecting quality
• which types of model attention are most sensitive to the number of attention heads and on which layers