Question Decomposition Tree for Answering Complex Questions over Knowledge Bases

Knowledge base question answering (KBQA) has attracted a lot of interest in recent years, especially for complex questions which require multiple facts to answer. Question decomposition is a promising way to answer complex questions. Existing decomposition methods split the question into sub-questions according to a single compositionality type, which is not sufficient for questions involving multiple compositionality types. In this paper, we propose Question Decomposition Tree (QDT) to represent the structure of complex questions. Inspired by recent advances in natural language generation (NLG), we present a two-staged method called Clue-Decipher to generate QDT. It can leverage the strong ability of NLG model and simultaneously preserve the original questions. To verify that QDT can enhance KBQA task, we design a decomposition-based KBQA system called QDTQA. Extensive experiments show that QDTQA outperforms previous state-of-the-art methods on ComplexWebQuestions dataset. Besides, our decomposition method improves an existing KBQA system by 12% and sets a new state-of-the-art on LC-QuAD 1.0.Comment: Accepted by AAAI202

How Does Online Information Influence Offline Transactions? Insights from Digital Real Estate Platforms

Digital platforms facilitate the flow of information and the execution of transactions. This study investigates the impact of signals from platform-provided online information regarding search and experience attributes of products on the prices of their offline transactions. We situate our theorizing and empirical work in the context of digital real estate platforms. Our results suggest that online information pertaining to properties’ experience attributes has a significant influence on the prices of offline property transactions. The amount of online information relating to experience attributes—specifically, length of textual property description and the number of photos—positively influences the sale price of a property. In contrast, the amount of online property information related to search attributes—specifically, facts and features—has no significant influence on the property’s sale price. In addition, online property information on experience attributes has a significant impact on the sale price of uncommon properties (those valued significantly above or below their neighborhood averages), whereas its impact on the price of common properties (those valued close to their neighborhood averages) is insignificant. The findings are robust to various model specifications and across property transactions in different years, seasons, and geographical regions. They are also neither subject to confounding effect of real estate agents’ service quality nor driven by unobserved property heterogeneities. The findings shed light on how signals from online property information are used by home buyers and sellers for different types of properties. The insights have implications for how real estate professionals can better utilize digital platforms to convey signals regarding properties and facilitate property transactions and for how the platforms can be designed to support the exchange of information that provides signals on the quality of offline goods that are highly risky and experiential.This accepted article is published as Zhengrui Jiang, Arun Rai, Hua Sun, Cheng Nie, Yuheng Hu (2023) How Does Online Information Influence Offline Transactions? Insights from Digital Real Estate Platforms. Information Systems Research 0(0). https://doi.org/10.1287/isre.2020.0658Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2020.0658. Copyright © 2023, INFORM

Negative Compressibility in Graphene-terminated Black Phosphorus Heterostructures

Negative compressibility generated by many-body effects in 2D electronic systems can enhance gate capacitance. We observe capacitance enhancement in a newly emerged 2D layered material, atomically thin black phosphorus (BP). The encapsulation of BP by hexagonal boron nitride sheets with few-layer graphene as a terminal ensures ultraclean heterostructure interfaces, allowing us to observe negative compressibility at low hole carrier concentrations. We explained the negative compressibility based on the Coulomb correlation among in-plane charges and their image charges in a gate electrode in the framework of Debye screening

MetaGPT: Meta Programming for Multi-Agent Collaborative Framework

Recently, remarkable progress has been made in automated task-solving through the use of multi-agents driven by large language models (LLMs). However, existing works primarily focuses on simple tasks lacking exploration and investigation in complicated tasks mainly due to the hallucination problem. This kind of hallucination gets amplified infinitely as multiple intelligent agents interact with each other, resulting in failures when tackling complicated problems.Therefore, we introduce MetaGPT, an innovative framework that infuses effective human workflows as a meta programming approach into LLM-driven multi-agent collaboration. In particular, MetaGPT first encodes Standardized Operating Procedures (SOPs) into prompts, fostering structured coordination. And then, it further mandates modular outputs, bestowing agents with domain expertise paralleling human professionals to validate outputs and reduce compounded errors. In this way, MetaGPT leverages the assembly line work model to assign diverse roles to various agents, thus establishing a framework that can effectively and cohesively deconstruct complex multi-agent collaborative problems. Our experiments conducted on collaborative software engineering tasks illustrate MetaGPT's capability in producing comprehensive solutions with higher coherence relative to existing conversational and chat-based multi-agent systems. This underscores the potential of incorporating human domain knowledge into multi-agents, thus opening up novel avenues for grappling with intricate real-world challenges. The GitHub repository of this project is made publicly available on: https://github.com/geekan/MetaGP

OsbZIP18, a Positive Regulator of Serotonin Biosynthesis, Negatively Controls the UV-B Tolerance in Rice

Serotonin (5-hydroxytryptamine) plays an important role in many developmental processes and biotic/abiotic stress responses in plants. Although serotonin biosynthetic pathways in plants have been uncovered, knowledge of the mechanisms of serotonin accumulation is still limited, and no regulators have been identified to date. Here, we identified the basic leucine zipper transcription factor OsbZIP18 as a positive regulator of serotonin biosynthesis in rice. Overexpression of OsbZIP18 strongly induced the levels of serotonin and its early precursors (tryptophan and tryptamine), resulting in stunted growth and dark-brown phenotypes. A function analysis showed that OsbZIP18 activated serotonin biosynthesis genes (including tryptophan decarboxylase 1 (OsTDC1), tryptophan decarboxylase 3 (OsTDC3), and tryptamine 5-hydroxylase (OsT5H)) by directly binding to the ACE-containing or G-box cis-elements in their promoters. Furthermore, we demonstrated that OsbZIP18 is induced by UV-B stress, and experiments using UV-B radiation showed that transgenic plants overexpressing OsbZIP18 exhibited UV-B stress-sensitive phenotypes. Besides, exogenous serotonin significantly exacerbates UV-B stress of OsbZIP18_OE plants, suggesting that the excessive accumulation of serotonin may be responsible for the sensitivity of OsbZIP18_OE plants to UV-B stress. Overall, we identified a positive regulator of serotonin biosynthesis and demonstrated that UV-B-stress induced serotonin accumulation, partly in an OsbZIP18-dependent manner