Motivations and Barriers Associated with the Adoption of Battery Electric Vehicles in Beijing: A Multinomial Logit Model Approach

The recent surge of the Chinese Plug-in Hybrid Electric Vehicle (PEV) market makes China the world’s largest PEV stock. A series of supportive policies in China contributed greatly to the rapid PEV adoption by limiting regular vehicles and reducing the price of PEVs. However, the role these policies play in changing references and encouraging consumers to purchase PEVs rather than conventional vehicles is not fully known. Other factors, rather than incentives, that could help maintain the current adoption trend are still unclear. The latter is especially critical in understanding how the market reacts to a gradually decreasing level of incentives to achieve the next goal of 5 million PEVs on the road by 2020 in China. Therefore, in this study the authors explored these research questions through a cross-sectional study of the current PEV market on consumers in Beijing by employing a multinomial logit model. Beijing has high levels of PEV adoptions in addition to a specific policy stimulus. The model results show significant influences of stimuli, individual socio-demographics, attitudes, charging infrastructure, and charging experiences on the adoption of PEVs over conventional vehicles. The results may help find out key interventions for policy makers to promote more PEV adoptions in China as well as other countries

Knowledge base question answering with a matching-aggregation model and question-specific contextual relations

National Research Foundation (NRF) Singapore under International Research Centres in Singapore Funding Initiativ

Prompting Large Language Models with Chain-of-Thought for Few-Shot Knowledge Base Question Generation

The task of Question Generation over Knowledge Bases (KBQG) aims to convert a logical form into a natural language question. For the sake of expensive cost of large-scale question annotation, the methods of KBQG under low-resource scenarios urgently need to be developed. However, current methods heavily rely on annotated data for fine-tuning, which is not well-suited for few-shot question generation. The emergence of Large Language Models (LLMs) has shown their impressive generalization ability in few-shot tasks. Inspired by Chain-of-Thought (CoT) prompting, which is an in-context learning strategy for reasoning, we formulate KBQG task as a reasoning problem, where the generation of a complete question is splitted into a series of sub-question generation. Our proposed prompting method KQG-CoT first retrieves supportive logical forms from the unlabeled data pool taking account of the characteristics of the logical form. Then, we write a prompt to explicit the reasoning chain of generating complicated questions based on the selected demonstrations. To further ensure prompt quality, we extend KQG-CoT into KQG-CoT+ via sorting the logical forms by their complexity. We conduct extensive experiments over three public KBQG datasets. The results demonstrate that our prompting method consistently outperforms other prompting baselines on the evaluated datasets. Remarkably, our KQG-CoT+ method could surpass existing few-shot SoTA results of the PathQuestions dataset by 18.25, 10.72, and 10.18 absolute points on BLEU-4, METEOR, and ROUGE-L, respectively.Comment: Accepted by EMNLP 2023 main conferenc

Multi-level head-wise match and aggregation in transformer for textual sequence matching

Transformer has been successfully applied to many natural language processing tasks. However, for textual sequence matching, simple matching between the representation of a pair of sequences might bring in unnecessary noise. In this paper, we propose a new approach to sequence pair matching with Transformer, by learning head-wise matching representations on multiple levels. Experiments show that our proposed approach can achieve new state-of-the-art performance on multiple tasks that rely only on pre-computed sequence-vector-representation, such as SNLI, MNLI-match, MNLI-mismatch, QQP, and SQuAD-binary.Comment: AAAI 2020, 8 page

R3^3 Prompting: Review, Rephrase and Resolve for Chain-of-Thought Reasoning in Large Language Models under Noisy Context

With the help of Chain-of-Thought (CoT) prompting, Large Language Models (LLMs) have achieved remarkable performance on various reasoning tasks. However, most of them have been evaluated under noise-free context and the dilemma for LLMs to produce inaccurate results under the noisy context has not been fully investigated. Existing studies utilize trigger sentences to encourage LLMs to concentrate on the relevant information but the trigger has limited effect on final answer prediction. Inspired by interactive CoT method, where intermediate reasoning steps are promoted by multiple rounds of interaction between users and LLMs, we propose a novel prompting method, namely R$^3$ prompting, for CoT reasoning under noisy context. Specifically, R$^3$ prompting interacts with LLMs to perform key sentence extraction, variable declaration and answer prediction, which corresponds to a thought process of reviewing, rephrasing and resolving. The responses generated at the last interaction will perform as hints to guide toward the responses of the next interaction. Our experiments show that R$^3$ prompting significantly outperforms existing CoT prompting methods on five reasoning tasks under noisy context. With GPT-3.5-turbo, we observe 3.7% accuracy improvement on average on the reasoning tasks under noisy context compared to the most competitive prompting baseline. More analyses and ablation studies show the robustness and generalization of R$^3$ prompting method in solving reasoning tasks in LLMs under noisy context

Power regeneration in the primary suspension of a railway vehicle

This paper presents an assessment of the potential for the use of power regenerating dampers (PRDs) in railway vehicle primary suspension systems equipped with the ‘Hybrid Mode’ and ‘Replace Mode’, and the evaluation of the potential/recoverable power that can be obtained. The power regenerating damper is configured as a hydraulic-electromagnetic based damper. Implications for ride comfort and running safety are also commented for investigating the performance of the suspension system. Several case studies of generic railway vehicle primary suspension systems are modelled and configured to include a power regenerating damper with two different configuration modes. Simulations are then carried out on track with typical irregularities for a generic UK passenger vehicle. The performance of the modified vehicle including regenerated power, ride comfort and running safety is evaluated. Analysis of key influencing factors are also carried out to examine their effects on power capability, ride comfort and running safety to guide the primary suspension design/specification

Aligning Large Language Models to a Domain-specific Graph Database

Graph Databases (Graph DB) are widely applied in various fields, including finance, social networks, and medicine. However, translating Natural Language (NL) into the Graph Query Language (GQL), commonly known as NL2GQL, proves to be challenging due to its inherent complexity and specialized nature. Some approaches have sought to utilize Large Language Models (LLMs) to address analogous tasks like text2SQL. Nevertheless, when it comes to NL2GQL taskson a particular domain, the absence of domain-specific NL-GQL data pairs makes it difficult to establish alignment between LLMs and the graph DB. To address this challenge, we propose a well-defined pipeline. Specifically, we utilize ChatGPT to create NL-GQL data pairs based on the given graph DB with self-instruct. Then, we use the created data to fine-tune LLMs, thereby achieving alignment between LLMs and the graph DB. Additionally, during inference, we propose a method that extracts relevant schema to the queried NL as the input context to guide LLMs for generating accurate GQLs.We evaluate our method on two constructed datasets deriving from graph DBs in finance domain and medicine domain, namely FinGQL and MediGQL. Experimental results demonstrate that our method significantly outperforms a set of baseline methods, with improvements of 5.90 and 6.36 absolute points on EM, and 6.00 and 7.09 absolute points on EX, respectively.Comment: 13 pages,2 figure