RocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking

In various natural language processing tasks, passage retrieval and passage re-ranking are two key procedures in finding and ranking relevant information. Since both the two procedures contribute to the final performance, it is important to jointly optimize them in order to achieve mutual improvement. In this paper, we propose a novel joint training approach for dense passage retrieval and passage re-ranking. A major contribution is that we introduce the dynamic listwise distillation, where we design a unified listwise training approach for both the retriever and the re-ranker. During the dynamic distillation, the retriever and the re-ranker can be adaptively improved according to each other's relevance information. We also propose a hybrid data augmentation strategy to construct diverse training instances for listwise training approach. Extensive experiments show the effectiveness of our approach on both MSMARCO and Natural Questions datasets. Our code is available at https://github.com/PaddlePaddle/RocketQA.Comment: EMNLP 202

Investigating the Factual Knowledge Boundary of Large Language Models with Retrieval Augmentation

Knowledge-intensive tasks (e.g., open-domain question answering (QA)) require a substantial amount of factual knowledge and often rely on external information for assistance. Recently, large language models (LLMs) (e.g., ChatGPT), have demonstrated impressive prowess in solving a wide range of tasks with world knowledge, including knowledge-intensive tasks. However, it remains unclear how well LLMs are able to perceive their factual knowledge boundaries, particularly how they behave when incorporating retrieval augmentation. In this study, we present an initial analysis of the factual knowledge boundaries of LLMs and how retrieval augmentation affects LLMs on open-domain QA. Specially, we focus on three primary research questions and analyze them by examining QA performance, priori judgement and posteriori judgement of LLMs. We show evidence that LLMs possess unwavering confidence in their capabilities to respond to questions and the accuracy of their responses. Furthermore, retrieval augmentation proves to be an effective approach in enhancing LLMs' awareness of knowledge boundaries, thereby improving their judgemental abilities. Additionally, we also find that LLMs have a propensity to rely on the provided retrieval results when formulating answers, while the quality of these results significantly impacts their reliance. The code to reproduce this work is available at https://github.com/RUCAIBox/LLM-Knowledge-Boundary

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Linking R&D strategy, national innovation system and FDI to firm performance

This paper investigates the role of R&D strategy, national innovation system (NIS) and foreign direct investment (FDI) in firm performance.Drawing on an institution-based view and the FDI spillover literature,we argue that firm performance is directly affected by R&D strategy, NIS and FDI spillovers. NIS also moderates FDI spillover effects on firm performance. Data analysis based on the World Bank Enterprise Survey of manufacturing firms in China in 2003 shows that the findings reinforce the hypotheses

Does Eco-Innovation of Emerging Market Firms Benefit from Knowledge Spillovers of MNC in a Multi-dimensional Task Environment?

Taking a socially proactive stance that aligns to their economic imperatives has led multinational corporations (MNCs) to focus on social innovation that tackles environmental challenges (or eco-innovation hereafter). Their knowledge of eco-innovation is important to emerging markets that are facing severe environmental challenges and to emerging market firms (EMFs) whose eco-innovation activities face resource and knowledge constraints. MNCs, through their foreign direct investment (FDI) activities in host emerging markets, can divulge economic, knowledge and environmental values of eco-innovation, helping EMFs to improve their eco-innovation through knowledge spillover channels. Taking the value-based approach, we draw on the eco-innovation research and the MNC/FDI spillovers literature to develop hypotheses on the relationship between regional knowledge spillovers of MNCs and the eco-innovation of EMFs in a multi-dimensional task environment characterized by munificence, complexity and dynamism. Our empirical examination is based on a sample of Chinese manufacturing firms during the period of 2003-2013. We find support to hypotheses that regional knowledge spillovers of MNCs enhance the positive effects of munificence and mitigate the negative effects of complexity and dynamism on the eco-innovation of EMFs

Surface modification of quartz sand: A review of its progress and its effect on heavy metal adsorption

Quartz sand (SiO2) is a prevalent filtration medium, boasting wide accessibility, superior stability, and cost-effectiveness. However, its utility is often curtailed by its sleek surface, limited active sites, and swift saturation of adsorption sites. This review outlines the prevalent strategies and agents for quartz sand surface modification and provides a comprehensive analysis of the various modification reagents and their operative mechanisms. It delves into the mechanism and utility of surface-modified quartz sand for adsorbing heavy metal ions (HMIs). It is found that the reported modifiers usually form connections with the surface of quartz sand through electrostatic forces, van der Waals forces, pore filling, chemical bonding, and/or molecular entanglement. The literature suggests that these modifications effectively address issues inherent to natural quartz sand, such as its low superficial coarseness, rapid adsorption site saturation, and limited adsorption capacity. Regrettably, comprehensive investigations into the particle size, regenerative capabilities, and application costs of surface-modified quartz sand and the critical factors for its wider adoption are lacking in most reports. The adsorption mechanisms indicate that surface-modified quartz sand primarily removes HMIs from aqueous solutions through surface complexation, ion exchange, and electrostatic and gravitational forces. However, these findings were derived under controlled laboratory conditions, and practical applications for treating real wastewater necessitate overcoming further laboratory-scale obstacles. Finally, this review outlines the limitations of partially surface modified quartz sand and suggests potential venues for future developments, providing a valuable reference for the advancement of cost-effective, HMI-absorbing, surface-modified quartz sand filter media