We expose a surprising failure of generalization in auto-regressive large language models (LLMs). If a model is trained on a sentence of the form ''_A_ is _B_'', it will not automatically generalize to the reverse direction ''_B_ is _A_''. This is the **Reversal Curse**. For instance, if a model is trained on ''Valentina Tereshkova was the first woman to travel to space'', it will not automatically be able to answer the question, ''Who was the first woman to travel to space?''. Moreover, the likelihood of the correct answer (''Valentina Tershkova'') will not be higher than for a random name. Thus, models do not generalize a prevalent pattern in their training set: if ''_A_ is _B_'' occurs, ''_B_ is _A_'' is more likely to occur. It is worth noting, however, that if ''_A_ is _B_'' appears _in-context_, models can deduce the reverse relationship. We provide evidence for the Reversal Curse by finetuning GPT-3 and Llama-1 on fictitious statements such as ''Uriah Hawthorne is the composer of _Abyssal Melodies_'' and showing that they fail to correctly answer ''Who composed _Abyssal Melodies?_''. The Reversal Curse is robust across model sizes and model families and is not alleviated by data augmentation. We also evaluate ChatGPT (GPT-3.5 and GPT-4) on questions about real-world celebrities, such as ''Who is Tom Cruise's mother? [A: Mary Lee Pfeiffer]'' and the reverse ''Who is Mary Lee Pfeiffer's son?''. GPT-4 correctly answers questions like the former 79\% of the time, compared to 33\% for the latter. Code available at: https://github.com/lukasberglund/reversal_curse.

Advances in neural modeling have achieved state-of-the-art (SOTA) results on public natural language processing (NLP) benchmarks, at times surpassing human performance.However, there is a gap between public benchmarks and real-world applications where noise, such as typographical or grammatical mistakes, is abundant and can result in degraded performance.Unfortunately, works which evaluate the robustness of neural models on noisy data and propose improvements, are limited to the English language.Upon analyzing noise in different languages, we observe that noise types vary greatly across languages.Thus, existing investigations do not generalize trivially to multilingual settings.To benchmark the performance of pretrained multilingual language models, we construct noisy datasets covering five languages and four NLP tasks and observe a clear gap in the performance between clean and noisy data in the zero-shot cross-lingual setting.After investigating several ways to boost the robustness of multilingual models in this setting, we propose Robust Contrastive Pretraining (RCP).RCP combines data augmentation with a contrastive loss term at the pretraining stage and achieves large improvements on noisy (& original test data) across two sentencelevel (+3.2%) and two sequence-labeling (+10 F1-score) multilingual classification tasks.Language Noise Injection Ratio Realistic Utt. % Realistic Examples (test-set) Unrealistic Examples (test-set) French (fr) 0.1 95.4% Me montré les vols directs de Charlotte à Minneapolis mardi matin .Quelle compagnie aérienne fut YX Me montré des vols entre Détroit er St. Louis sur Delta Northwest US Air est United Airlines .Lister des vols de Las Vegas à Son Diego German (de) 0.2 94.5% Zeige mir der Flüge zwischen Housten und Orlando Welche Flüge gibt es vom Tacoma nach San Jose Zeige mit alle Flüge vor Charlotte nach Minneapolis zum Dienstag morgen Zeige mit Flüge an Milwaukee nach Washington DC v. 12 Uhr

Parameter-efficient fine-tuning methods (PEFTs) offer the promise of adapting large pre-trained models while only tuning a small number of parameters.They have been shown to be competitive with full model fine-tuning for many downstream tasks.However, prior work indicates that PEFTs may not work as well for machine translation (MT), and there is no comprehensive study showing when PEFTs work for MT.We conduct a comprehensive empirical study of PEFTs for MT, considering (1) various parameter budgets, (2) a diverse set of language-pairs, and (3) different pre-trained models.We find that 'adapters', in which small feed-forward networks are added after every layer, are indeed on par with full model fine-tuning when the parameter budget corresponds to 10% of total model parameters.Nevertheless, as the number of tuned parameters decreases, the performance of PEFTs decreases.The magnitude of this decrease depends on the language pair, with PEFTs particularly struggling for distantly related language-pairs.We find that using PEFTs with a larger pre-trained model outperforms full fine-tuning with a smaller model, and for smaller training data sizes, PEFTs outperform full fine-tuning for the same pre-trained model. 1

There has been recent success in pre-training on monolingual data and fine-tuning on Machine Translation (MT), but it remains unclear how to best leverage a pre-trained model for a given MT task.This paper investigates the benefits and drawbacks of freezing parameters, and adding new ones, when fine-tuning a pre-trained model on MT.We focus on 1) Fine-tuning a model trained only on English monolingual data, BART.2) Fine-tuning a model trained on monolingual data from 25 languages, mBART.For BART we get the best performance by freezing most of the model parameters, and adding extra positional embeddings.For mBART we match or outperform the performance of naive fine-tuning for most language pairs with the encoder, and most of the decoder, frozen.The encoder-decoder attention parameters are most important to finetune.When constraining ourselves to an outof-domain training set for Vietnamese to English we see the largest improvements over the fine-tuning baseline.

The Transformer model has achieved state-of-the-art performance in many sequence modeling tasks. However, how to leverage model capacity with large or variable depths is still an open challenge. We present a probabilistic framework to automatically learn which layer(s) to use by learning the posterior distributions of layer selection. As an extension of this framework, we propose a novel method to train one shared Transformer network for multilingual machine translation with different layer selection posteriors for each language pair. The proposed method alleviates the vanishing gradient issue and enables stable training of deep Transformers (e.g. 100 layers). We evaluate on WMT English-German machine translation and masked language modeling tasks, where our method outperforms existing approaches for training deeper Transformers. Experiments on multilingual machine translation demonstrate that this approach can effectively leverage increased model capacity and bring universal improvement for both many-to-one and one-to-many translation with diverse language pairs.

Multi-task learning shares information between related tasks, sometimes reducing the number of parameters required. State-of-the-art results across multiple natural language understanding tasks in the GLUE benchmark have previously used transfer from a single large task: unsupervised pre-training with BERT, where a separate BERT model was fine-tuned for each task. We explore multi-task approaches that share a \hbox{single} BERT model with a small number of additional task-specific parameters. Using new adaptation modules, PALs or ‘projected attention layers’, we match the performance of separately fine-tuned models on the GLUE benchmark with $\approx$7 times fewer parameters, and obtain state-of-the-art results on the Recognizing Textual Entailment dataset.