SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages

Text simplification research has mostly focused on sentence-level simplification, even though many desirable edits - such as adding relevant background information or reordering content - may require document-level context. Prior work has also predominantly framed simplification as a single-step, input-to-output task, only implicitly modeling the fine-grained, span-level edits that elucidate the simplification process. To address both gaps, we introduce the SWiPE dataset, which reconstructs the document-level editing process from English Wikipedia (EW) articles to paired Simple Wikipedia (SEW) articles. In contrast to prior work, SWiPE leverages the entire revision history when pairing pages in order to better identify simplification edits. We work with Wikipedia editors to annotate 5,000 EW-SEW document pairs, labeling more than 40,000 edits with proposed 19 categories. To scale our efforts, we propose several models to automatically label edits, achieving an F-1 score of up to 70.6, indicating that this is a tractable but challenging NLU task. Finally, we categorize the edits produced by several simplification models and find that SWiPE-trained models generate more complex edits while reducing unwanted edits.Comment: ACL 2023, Long Pape

Combined Deficiency of p50 and cRel in CD4+ T Cells Reveals an Essential Requirement for Nuclear Factor κB in Regulating Mature T Cell Survival and In Vivo Function

Signaling pathways involved in regulating T cell proliferation and survival are not well understood. Here we have investigated a possible role of the nuclear factor (NF)-κB pathway in regulating mature T cell function by using CD4+ T cells from p50−/− cRel−/− mice, which exhibit virtually no inducible κB site binding activity. Studies with these mice indicate an essential role of T cell receptor (TCR)-induced NF-κB in regulating interleukin (IL)-2 expression, cell cycle entry, and survival of T cells. Our results further indicate that NF-κB regulates TCR-induced expression of antiapoptotic Bcl-2 family members. Strikingly, retroviral transduction of CD4+ T cells with the NF-κB–inducing IκB kinase β showed that NF-κB activation is not only necessary but also sufficient for T cell survival. In contrast, our results indicate a lack of involvement of NF-κB in both IL-2 and Akt-induced survival pathways. In vivo, p50−/− cRel−/− mice showed impaired superantigen-induced T cell responses as well as decreased numbers of effector/memory and regulatory CD4+ T cells. These findings provide the first demonstration of a role for NF-κB proteins in regulating T cell function in vivo and establish a critically important function of NF-κB in TCR-induced regulation of survival

Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning

Large language models (LLMs) have shown impressive performance in following natural language instructions to solve unseen tasks. However, it remains unclear whether models truly understand task definitions and whether the human-written definitions are optimal. In this paper, we systematically study the role of task definitions in instruction learning. We first conduct an ablation analysis informed by human annotations to understand which parts of a task definition are most important, and find that model performance only drops substantially when removing contents describing the task output, in particular label information. Next, we propose an automatic algorithm to compress task definitions to a minimal supporting set of tokens, and find that 60\% of tokens can be removed while maintaining or even improving model performance. Based on these results, we propose two strategies to help models better leverage task instructions: (1) providing only key information for tasks in a common structured format, and (2) adding a meta-tuning stage to help the model better understand the definitions. With these two strategies, we achieve a 4.2 Rouge-L improvement over 119 unseen test tasks.Comment: ACL 2023, camera-ready; 10 page

Tag expression: Tagging with feeling

ABSTRACT In this paper we introduce tag expression, a novel form of preference elicitation that combines elements from tagging and rating systems. Tag expression enables users to apply affect to tags to indicate whether the tag describes a reason they like, dislike, or are neutral about a particular item. We present a user interface for applying affect to tags, as well as a technique for visualizing the overall community's affect. By analyzing 27,773 tag expressions from 553 users entered in a 3-month period, we empirically evaluate our design choices. We also present results of a survey of 97 users that explores users' motivations in tagging and measures user satisfaction with tag expression