ChatGPT is a Knowledgeable but Inexperienced Solver: An Investigation of Commonsense Problem in Large Language Models

Large language models (LLMs) such as ChatGPT and GPT-4 have made significant progress in NLP. However, their ability to memorize, represent, and leverage commonsense knowledge has been a well-known pain point for LLMs. It remains unclear that: (1) Can GPTs effectively answer commonsense questions? (2) Are GPTs knowledgeable in commonsense? (3) Are GPTs aware of the underlying commonsense knowledge for answering a specific question? (4) Can GPTs effectively leverage commonsense for answering questions? To evaluate the above commonsense problems, we conduct a series of experiments to evaluate ChatGPT's commonsense abilities, and the experimental results show that: (1) GPTs can achieve good QA accuracy in commonsense tasks, while they still struggle with certain types of knowledge. (2) ChatGPT is knowledgeable, and can accurately generate most of the commonsense knowledge using knowledge prompts. (3) Despite its knowledge, ChatGPT is an inexperienced commonsense problem solver, which cannot precisely identify the needed commonsense knowledge for answering a specific question, i.e., ChatGPT does not precisely know what commonsense knowledge is required to answer a question. The above findings raise the need to investigate better mechanisms for utilizing commonsense knowledge in LLMs, such as instruction following, better commonsense guidance, etc

Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting

Incorporating factual knowledge in knowledge graph is regarded as a promising approach for mitigating the hallucination of large language models (LLMs). Existing methods usually only use the user's input to query the knowledge graph, thus failing to address the factual hallucination generated by LLMs during its reasoning process. To address this problem, this paper proposes Knowledge Graph-based Retrofitting (KGR), a new framework that incorporates LLMs with KGs to mitigate factual hallucination during the reasoning process by retrofitting the initial draft responses of LLMs based on the factual knowledge stored in KGs. Specifically, KGR leverages LLMs to extract, select, validate, and retrofit factual statements within the model-generated responses, which enables an autonomous knowledge verifying and refining procedure without any additional manual efforts. Experiments show that KGR can significantly improve the performance of LLMs on factual QA benchmarks especially when involving complex reasoning processes, which demonstrates the necessity and effectiveness of KGR in mitigating hallucination and enhancing the reliability of LLMs

Two-Stage Water Jet Landing Point Prediction Model for Intelligent Water Shooting Robot

In this paper, an intelligent water shooting robot system for situations of carrier shake and target movement is designed, which uses a 2 DOF (degree of freedom) robot as an actuator, a photoelectric camera to detect and track the desired target, and a gyroscope to keep the robot’s body stable when it is mounted on the motion carriers. Particularly, for the accurate shooting of the designed system, an online tuning model of the water jet landing point based on the back-propagation algorithm was proposed. The model has two stages. In the first stage, the polyfit function of Matlab is used to fit a model that satisfies the law of jet motion in ideal conditions without interference. In the second stage, the model uses the back-propagation algorithm to update the parameters online according to the visual feedback of the landing point position. The model established by this method can dynamically eliminate the interference of external factors and realize precise on-target shooting. The simulation results show that the model can dynamically adjust the parameters according to the state relationship between the landing point and the desired target, which keeps the predicted pitch angle error within 0.1°. In the test on the actual platform, when the landing point is 0.5 m away from the position of the desired target, the model only needs 0.3 s to adjust the water jet to hit the target. Compared to the state-of-the-art method, GA-BP (genetic algorithm-back-propagation), the proposed method’s predicted pitch angle error is within 0.1 degree with 1/4 model parameters, while costing 1/7 forward propagation time and 1/200 back-propagation calculation time

Investigation on Potential-Induced Degradation in a 50 MWp Crystalline Silicon Photovoltaic Power Plant

The regular performance deterioration of P-type crystalline silicon solar modules and module strings caused by potential-induced degradation in a photovoltaic power plant was found in the field. The PID-affected solar modules dismounted from the photovoltaic power plant were further investigated systematically in the laboratory. For the first time, we found that the neutral point of voltage in a module string moved forward to the positive pole for a PID-affected module string as time goes on. Even if low positive voltage is applied to a PID-prone module, it could cause PID. The thermographic and electroluminescence (EL) images of a PID-affected module string also exhibit a regular degradation pattern. This is in good agreement with the measured power loss of the dismounted solar modules under standard test conditions. The results obtained in this paper show that the maximum power degradation rate of solar modules was as high as 53.26% after only one year of operation because of PID in the field. Due to the vast amount of solar modules and incomplete recovery, this is a terrible catastrophe for the owner of a power plant and module producer

Comparative study of two degree-of-freedom rotary-linear machines with permanent-magnet mover for high dynamic performance

This paper analyzes and evaluates various topologies of yokeless rotary-linear (RL) machine with permanent-magnet (PM) mover targeting for the achieving high dynamic performance. For the conventional yokeless RL machines, the single-stator topology features limited slot area for armature winding excited for both motions, and the coupled magnetic fields of both motions requires complex control strategy. In order to mitigate these issues, a double-stator RL machine with hybrid PMs array, which adopts the PMs with six magnetization directions, is proposed in this paper. An improved PMs array is introduced for the double-stator topology to achieve decoupled control for both motions. In addition, an analytical model of the radial flux density is derived to reveal the characteristics of the magnetic fields in different topologies. The parametric studies towards key dimensions are conducted to provide design guidelines and obtain the optimal design. Finally, the electromagnetic performances of the proposed design are compared systematically with the conventional single-stator counterparts. It is revealed that the proposed topology can enhance the performances in both motions, namely rotary and linear motions.Ministry of Education (MOE)Published versionThis work was supported by the Ministry of Education (MOE), Singapore, under its MOE Academic Research Fund (AcRF) Tier 1 Program under Grant 2019-T1-002-064

A two degree-of-freedom rotary-linear machine with transverse-flux structure

This paper proposes a two-degree-of-freedom rotary-linear machine with transverse-flux structure. In the proposed machine, the rotary flux and linear flux are naturally decoupled, hence its linear force and rotary torque can be controlled independently. Unlike the conventional rotary-linear machines with 3D-flux pattern, the proposed machine with transverse-flux structure can employ circumferentially laminated steel sheets to provide outstanding electromagnetic performance. The topology and operation principle of the proposed machine are introduced, while the naturally decoupled-flux feature is explained by analytical modelling and 3D finite-element method. A parametric study of pole-pair numbers combination is conducted to obtain the optimal pole-pair numbers in both axial and circumferential directions. The optimal electromagnetic performances are quantitatively compared with other two conventional rotary-linear machines, while an experimental prototype is manufactured to verify the proposed concepts.Ministry of Education (MOE)National Research Foundation (NRF)Submitted/Accepted versionThis work was supported by National Research Foundation (NRF) Singapore under its NRF Fellowship Grant NRF-NRFF12-2020-0003, and the Ministry of Education, Singapore, under its Academic Research Fund (AcRF) Tier 1 Program under Grant 2019-T1-002-064

A Drop of Ink may Make a Million Think: The Spread of False Information in Large Language Models

Large language models (LLMs) like ChatGPT have gained increasing prominence in artificial intelligence, making a profound impact on society and various industries like business and science. However, the presence of false information on the internet and in text corpus poses a significant risk to the reliability and safety of LLMs, underscoring the urgent need to understand the mechanisms of how false information impacts and spreads in LLMs. In this paper, we investigate how false information spreads in LLMs and affects related responses by conducting a series of experiments on the effects of source authority, injection paradigm, and information relevance. Specifically, we compare four authority levels of information sources (Twitter, web blogs, news reports, and research papers), two common knowledge injection paradigms (in-context injection and learning-based injection), and three degrees of information relevance (direct, indirect, and peripheral). The experimental results show that (1) False information will spread and contaminate related memories in LLMs via a semantic diffusion process, i.e., false information has global detrimental effects beyond its direct impact. (2) Current LLMs are susceptible to authority bias, i.e., LLMs are more likely to follow false information presented in a trustworthy style like news or research papers, which usually causes deeper and wider pollution of information. (3) Current LLMs are more sensitive to false information through in-context injection than through learning-based injection, which severely challenges the reliability and safety of LLMs even if all training data are trusty and correct. The above findings raise the need for new false information defense algorithms to address the global impact of false information, and new alignment algorithms to unbiasedly lead LLMs to follow internal human values rather than superficial patterns