Large language models are able to exploit in-context learning to access external knowledge beyond their training data through retrieval-augmentation. While promising, its inner workings remain unclear. In this work, we shed light on the mechanism of in-context retrieval augmentation for question answering by viewing a prompt as a composition of informational components. We propose an attribution-based method to identify specialized attention heads, revealing in-context heads that comprehend instructions and retrieve relevant contextual information, and parametric heads that store entities' relational knowledge. To better understand their roles, we extract function vectors and modify their attention weights to show how they can influence the answer generation process. Finally, we leverage the gained insights to trace the sources of knowledge used during inference, paving the way towards more safe and transparent language models.

Incremental models that process sentences one token at a time will sometimes encounter points where more than one interpretation is possible.Causal models are forced to output one interpretation and continue, whereas models that can revise may edit their previous output as the ambiguity is resolved.In this work, we look at how restart-incremental Transformers build and update internal states, in an effort to shed light on what processes cause revisions not viable in autoregressive models.We propose an interpretable way to analyse the incremental states, showing that their sequential structure encodes information on the garden path effect and its resolution.Our method brings insights on various bidirectional encoders for contextualised meaning representation and dependency parsing, contributing to show their advantage over causal models when it comes to revisions. 1

Incremental dialogue model components produce a sequence of output prefixes based on incoming input.Mistakes can occur due to local ambiguities or to wrong hypotheses, making the ability to revise past outputs a desirable property that can be governed by a policy.In this work, we formalise and characterise edits and revisions in incremental sequence labelling and propose metrics to evaluate revision policies.We then apply our methodology to profile the incremental behaviour of three Transformerbased encoders in various tasks, paving the road for better revision policies.

Incremental processing allows interactive systems to respond based on partial inputs, which is a desirable property e.g. in dialogue agents.The currently popular Transformer architecture inherently processes sequences as a whole, abstracting away the notion of time.Recent work attempts to apply Transformers incrementally via restart-incrementality by repeatedly feeding, to an unchanged model, increasingly longer input prefixes to produce partial outputs.However, this approach is computationally costly and does not scale efficiently for long sequences.In parallel, we witness efforts to make Transformers more efficient, e.g. the Linear Transformer (LT) with a recurrence mechanism.In this work, we examine the feasibility of LT for incremental NLU in English.Our results show that the recurrent LT model has better incremental performance and faster inference speed compared to the standard Transformer and LT with restartincrementality, at the cost of part of the nonincremental (full sequence) quality.We show that the performance drop can be mitigated by training the model to wait for right context before committing to an output and that training with input prefixes is beneficial for delivering correct partial outputs.