Abstract Text-To-Image (TTI) models, such as DALL-E and StableDiffusion, have demonstrated remarkable prompt-based image generation capabilities. Multilingual encoders may have a substantial impact on the cultural agency of these models, as language is a conduit of culture. In this study, we explore the cultural perception embedded in TTI models by characterizing culture across three tiers: cultural dimensions, cultural domains, and cultural concepts. Based on this ontology, we derive prompt templates to unlock the cultural knowledge in TTI models, and propose a comprehensive suite of evaluation techniques, including intrinsic evaluations using the CLIP space, extrinsic evaluations with a Visual-Question-Answer models and human assessments, to evaluate the cultural content of TTI-generated images. To bolster our research, we introduce the CulText2I dataset, based on six diverse TTI models and spanning ten languages. Our experiments provide insights regarding Do, What, Which, and How research questions about the nature of cultural encoding in TTI models, paving the way for cross-cultural applications of these models.1

Large Language Models (LLMs) often do not perform well on queries that require the aggregation of information across texts. To better evaluate this setting and facilitate modeling efforts, we introduce TACT - Text And Calculations through Tables, a dataset crafted to evaluate LLMs' reasoning and computational abilities using complex instructions. TACT contains challenging instructions that demand stitching information scattered across one or more texts, and performing complex integration on this information to generate the answer. We construct this dataset by leveraging an existing dataset of texts and their associated tables. For each such tables, we formulate new queries, and gather their respective answers. We demonstrate that all contemporary LLMs perform poorly on this dataset, achieving an accuracy below 38%. To pinpoint the difficulties and thoroughly dissect the problem, we analyze model performance across three components: table-generation, Pandas command-generation, and execution. Unexpectedly, we discover that each component presents substantial challenges for current LLMs. These insights lead us to propose a focused modeling framework, which we refer to as IE as a tool. Specifically, we propose to add "tools" for each of the above steps, and implement each such tool with few-shot prompting. This approach shows an improvement over existing prompting techniques, offering a promising direction for enhancing model capabilities in these tasks.

Natural language processing (NLP) algorithms have become very successful, but they still struggle when applied to out-of-distribution examples.In this paper we propose a controllable generation approach in order to deal with this domain adaptation (DA) challenge.Given an input text example, our DoCoGen algorithm generates a domain-counterfactual textual example (D-CON) -that is similar to the original in all aspects, including the task label, but its domain is changed to a desired one.Importantly, DoCoGen is trained using only unlabeled examples from multiple domainsno NLP task labels or parallel pairs of textual examples and their domain-counterfactuals are required.We show that DoCoGen can generate coherent counterfactuals consisting of multiple sentences.We use the D-CONs generated by DoCoGen to augment a sentiment classifier and a multi-label intent classifier in 20 and 78 DA setups, respectively, where source-domain labeled data is scarce.Our model outperforms strong baselines and improves the accuracy of a state-of-the-art unsupervised DA algorithm.1 * Both authors equally contributed to this work.Original, Kitchen: A good knife but Quality Control was poor.The knife is solid and very comfortable in hand, however, when I got it new, the blade is slightly bent.I expect it to be in almost perfect condition, but it's not.DoCoGen, Kitchen → Electronics: A good product but Quality Control was poor.The ipod is very easy to use and very comfortable in hand, however, when I got it new, the ipod is slightly flimsy.I expect it to be in almost perfect shape, but it's not.Original, DVD: The direction of this film is excellent.I love all the characters and the way they interact.The storyline is very important also.It's about religious beliefs and neighbors that interact with each other.It's a well-paced and interesting story that's not like anything else I've ever seen.DoCoGen, DVD → Airline: The service on this flight is excellent.I love the staff and the way they interact.The safety is very important also.It's nice to have staff and neighbors that can help each other.It's a well-groomed and professional crew that's not like anything else I've ever experienced.Original, Electronics: That relay board is only good for switching AC loads of 100V or more.If you have a lower voltage load, it's not going to work.For low voltage loads use transistors, MOSFETs or a ULN2803 driver board.DoCoGen, Electronics → Statistics: That model is only good for data of $n$ or more.If you have a lower $n$, it's not going to work.For lower $n$ regression use a linear, logistic or a t-test.

Abstract Natural Language Processing algorithms have made incredible progress, but they still struggle when applied to out-of-distribution examples. We address a challenging and underexplored version of this domain adaptation problem, where an algorithm is trained on several source domains, and then applied to examples from unseen domains that are unknown at training time. Particularly, no examples, labeled or unlabeled, or any other knowledge about the target domain are available to the algorithm at training time. We present PADA: An example-based autoregressive Prompt learning algorithm for on-the-fly Any-Domain Adaptation, based on the T5 language model. Given a test example, PADA first generates a unique prompt for it and then, conditioned on this prompt, labels the example with respect to the NLP prediction task. PADA is trained to generate a prompt that is a token sequence of unrestricted length, consisting of Domain Related Features (DRFs) that characterize each of the source domains. Intuitively, the generated prompt is a unique signature that maps the test example to a semantic space spanned by the source domains. In experiments with 3 tasks (text classification and sequence tagging), for a total of 14 multi-source adaptation scenarios, PADA substantially outperforms strong baselines.1

Pivot-based neural representation models have led to significant progress in domain adaptation for NLP. However, previous research following this approach utilize only labeled data from the source domain and unlabeled data from the source and target domains, but neglect to incorporate massive unlabeled corpora that are not necessarily drawn from these domains. To alleviate this, we propose PERL: A representation learning model that extends contextualized word embedding models such as BERT (Devlin et al., 2019 ) with pivot-based fine-tuning. PERL outperforms strong baselines across 22 sentiment classification domain adaptation setups, improves in-domain model performance, yields effective reduced-size models, and increases model stability.1