Neural Semantic Parsing in Low-Resource Settings with Back-Translation and Meta-Learning

Neural semantic parsing has achieved impressive results in recent years, yet its success relies on the availability of large amounts of supervised data. Our goal is to learn a neural semantic parser when only prior knowledge about a limited number of simple rules is available, without access to either annotated programs or execution results. Our approach is initialized by rules, and improved in a back-translation paradigm using generated question-program pairs from the semantic parser and the question generator. A phrase table with frequent mapping patterns is automatically derived, also updated as training progresses, to measure the quality of generated instances. We train the model with model-agnostic meta-learning to guarantee the accuracy and stability on examples covered by rules, and meanwhile acquire the versatility to generalize well on examples uncovered by rules. Results on three benchmark datasets with different domains and programs show that our approach incrementally improves the accuracy. On WikiSQL, our best model is comparable to the SOTA system learned from denotations

WizardLM: Empowering Large Language Models to Follow Complex Instructions

Training large language models (LLM) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming and labor-intensive. Moreover, humans may struggle to produce high-complexity instructions. In this paper, we show an avenue for creating large amounts of instruction data with varying levels of complexity using LLM instead of humans. Starting with an initial set of instructions, we use our proposed Evol-Instruct to rewrite them step by step into more complex instructions. Then, we mix all generated instruction data to fine-tune LLaMA. We call the resulting model WizardLM. Human evaluations on a complexity-balanced test bed show that instructions from Evol-Instruct are superior to human-created ones. By analyzing the human evaluation results of the high complexity part, we demonstrate that outputs from our WizardLM model are preferred to outputs from OpenAI ChatGPT. Even though WizardLM still lags behind ChatGPT in some aspects, our findings suggest that fine-tuning with AI-evolved instructions is a promising direction for enhancing large language models. Our codes and generated data are public at https://github.com/nlpxucan/WizardLMComment: large language model, instruction fine-tun

Synergistic Interplay between Search and Large Language Models for Information Retrieval

Information retrieval (IR) plays a crucial role in locating relevant resources from vast amounts of data, and its applications have evolved from traditional knowledge bases to modern retrieval models (RMs). The emergence of large language models (LLMs) has further revolutionized the IR field by enabling users to interact with search systems in natural languages. In this paper, we explore the advantages and disadvantages of LLMs and RMs, highlighting their respective strengths in understanding user-issued queries and retrieving up-to-date information. To leverage the benefits of both paradigms while circumventing their limitations, we propose InteR, a novel framework that facilitates information refinement through synergy between RMs and LLMs. InteR allows RMs to expand knowledge in queries using LLM-generated knowledge collections and enables LLMs to enhance prompt formulation using retrieved documents. This iterative refinement process augments the inputs of RMs and LLMs, leading to more accurate retrieval. Experiments on large-scale retrieval benchmarks involving web search and low-resource retrieval tasks demonstrate that InteR achieves overall superior zero-shot retrieval performance compared to state-of-the-art methods, even those using relevance judgment. Source code is available at https://github.com/Cyril-JZ/InteRComment: Pre-print. Work in progres

A study of Multistage/Multifunction Column for Fine Coal Cleaning CRADA PC93-005, Final Report

The overall objective of the this research project is to explore the potential applicability of a multistage column for fine coal cleaning and other applications in fluid particle separation. The research work identifies the design parameters and their effects on the performance of the separation device. The results of this study provide an engineering data basis for further development of this technology in coal cleaning and in general areas of fluid and particle separations

Mechanism, structural and functional insights into nidovirus-induced double-membrane vesicles

During infection, positive-stranded RNA causes a rearrangement of the host cell membrane, resulting in specialized membrane structure formation aiding viral genome replication. Double-membrane vesicles (DMVs), typical structures produced by virus-induced membrane rearrangements, are platforms for viral replication. Nidoviruses, one of the most complex positive-strand RNA viruses, have the ability to infect not only mammals and a few birds but also invertebrates. Nidoviruses possess a distinctive replication mechanism, wherein their nonstructural proteins (nsps) play a crucial role in DMV biogenesis. With the participation of host factors related to autophagy and lipid synthesis pathways, several viral nsps hijack the membrane rearrangement process of host endoplasmic reticulum (ER), Golgi apparatus, and other organelles to induce DMV formation. An understanding of the mechanisms of DMV formation and its structure and function in the infectious cycle of nidovirus may be essential for the development of new and effective antiviral strategies in the future