To produce accurate predictions, language models (LMs) must balance between generalization and memorization.Yet, little is known about the mechanism by which transformer LMs employ their memorization capacity.When does a model decide to output a memorized phrase, and how is this phrase then retrieved from memory?In this work, we offer the first methodological framework for probing and characterizing recall of memorized sequences in transformer LMs.First, we lay out criteria for detecting model inputs that trigger memory recall, and propose idioms as inputs that typically fulfill these criteria.Next, we construct a dataset of English idioms and use it to compare model behavior on memorized vs. non-memorized inputs.Specifically, we analyze the internal prediction construction process by interpreting the model's hidden representations as a gradual refinement of the output probability distribution.We find that across different model sizes and architectures, memorized predictions are a two-step process: early layers promote the predicted token to the top of the output distribution, and upper layers increase model confidence.This suggests that memorized information is stored and retrieved in the early layers of the network.Last, we demonstrate the utility of our methodology beyond idioms in memorized factual statements.Overall, our work makes a first step towards understanding memory recall, and provides a methodological basis for future studies of transformer memorization.1

Uri Shaham, Elad Segal, Maor Ivgi, Avia Efrat, Ori Yoran, Adi Haviv, Ankit Gupta, Wenhan Xiong, Mor Geva, Jonathan Berant, Omer Levy. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022.

Large pre-trained language models have been shown to encode large amounts of world and commonsense knowledge in their parameters, leading to substantial interest in methods for extracting that knowledge.In past work, knowledge was extracted by taking manuallyauthored queries and gathering paraphrases for them using a separate pipeline.In this work, we propose a method for automatically rewriting queries into "BERTese", a paraphrase query that is directly optimized towards better knowledge extraction.To encourage meaningful rewrites, we add auxiliary loss functions that encourage the query to correspond to actual language tokens.We empirically show our approach outperforms competing baselines, obviating the need for complex pipelines.Moreover, BERTese provides some insight into the type of language that helps language models perform knowledge extraction.

Latent alignment objectives such as CTC and AXE significantly improve non-autoregressive machine translation models.Can they improve autoregressive models as well?We explore the possibility of training autoregressive machine translation models with latent alignment objectives, and observe that, in practice, this approach results in degenerate models.We provide a theoretical explanation for these empirical results, and prove that latent alignment objectives are incompatible with teacher forcing.