Retrieve-and-Read: Multi-task Learning of Information Retrieval and Reading Comprehension

This study considers the task of machine reading at scale (MRS) wherein, given a question, a system first performs the information retrieval (IR) task of finding relevant passages in a knowledge source and then carries out the reading comprehension (RC) task of extracting an answer span from the passages. Previous MRS studies, in which the IR component was trained without considering answer spans, struggled to accurately find a small number of relevant passages from a large set of passages. In this paper, we propose a simple and effective approach that incorporates the IR and RC tasks by using supervised multi-task learning in order that the IR component can be trained by considering answer spans. Experimental results on the standard benchmark, answering SQuAD questions using the full Wikipedia as the knowledge source, showed that our model achieved state-of-the-art performance. Moreover, we thoroughly evaluated the individual contributions of our model components with our new Japanese dataset and SQuAD. The results showed significant improvements in the IR task and provided a new perspective on IR for RC: it is effective to teach which part of the passage answers the question rather than to give only a relevance score to the whole passage.Comment: 10 pages, 6 figure. Accepted as a full paper at CIKM 201

Dual Catalysis of Gold Nanoclusters: Photocatalytic Cross-Dehydrogenative Coupling by Cooperation of Superatomic Core and Molecularly Modified Staples

金ナノクラスターの二重触媒特性の発見 --超原子コアと分子修飾ステープルの協働効果--. 京都大学プレスリリース. 2023-11-14.Thiolate-protected gold nanoclusters (AuNCs) have attracted significant attention as nano-catalysts, revealing a superatomic core and gold-thiolate staples as distinct structural units. Here, we demonstrate the unprecedented dual catalytic activity of thiolate-protected [Au₂₅(SR)₁₈]⁻ nanoclusters, involving both photosensitized ¹O₂ generation by the Au₁₃ superatomic core and catalytic carbon–carbon bond formation facilitated by Au₂(SR)₃ staples. This synergistic combination of two different catalytic units enables efficient cross-dehydrogenative coupling of terminal alkynes and tertiary aliphatic amines to afford propargylamines in high yields of up to 93%. Mixed-ligand AuNCs bearing both thiolate and alkynyl ligands revealed the intermediacy of the alkynyl-exchanged AuNCs toward both photosensitization and C–C bond-forming catalytic cycles. Density functional theory calculations also supported the intermediacy of the alkynyl-exchanged AuNCs. Thus, the use of ligand-protected metal nanoclusters has enabled the development of an exceptional multifunctional catalyst, wherein distinct nanocluster components facilitate cooperative photo- and chemo-catalysis

InstructDoc: A Dataset for Zero-Shot Generalization of Visual Document Understanding with Instructions

We study the problem of completing various visual document understanding (VDU) tasks, e.g., question answering and information extraction, on real-world documents through human-written instructions. To this end, we propose InstructDoc, the first large-scale collection of 30 publicly available VDU datasets, each with diverse instructions in a unified format, which covers a wide range of 12 tasks and includes open document types/formats. Furthermore, to enhance the generalization performance on VDU tasks, we design a new instruction-based document reading and understanding model, InstructDr, that connects document images, image encoders, and large language models (LLMs) through a trainable bridging module. Experiments demonstrate that InstructDr can effectively adapt to new VDU datasets, tasks, and domains via given instructions and outperforms existing multimodal LLMs and ChatGPT without specific training.Comment: Accepted by AAAI2024; project page: https://github.com/nttmdlab-nlp/InstructDo