arXiv subjects

Hao Ming

Publications and source records attributed to Hao Ming.

4 recordsLinked to original sources

arXiv2025

We Need Knowledge Distillation for Solving Math Word Problems

The enhancement of mathematical capabilities in large language models (LLMs) fosters new developments in mathematics education within primary and secondary schools, particularly as they relate to intelligent tutoring systems. However, LLMs require substantial computational resources, resulting in significant costs in educational contexts. To mitigate this drawback, this paper investigates the feasibility of compressing LLMs for solving math word problems (MWPs). We compress the embedded vectors encoded by BERT and distill a considerably smaller student model. Our findings indicate that the student model can maintain nearly 90% of the performance of the teacher model while utilizing only 1/12 of its parameters. In addition to achieving high accuracy, the model exhibits strong generalizability, as the compressed vectors perform well across all tasks related to MWPs, and the distillation process is not task-specific. The success of this distillation demonstrates that the underlying principles are generic and not limited to a specific task. We further explore the reasons behind the compressibility of embedded vectors, revealing that part-of-speech information, rather than entity recognition, is crucial for MWPs, which may significantly contribute to their compressibility. The improvements in efficiency and cost reduction provide substantial value for intelligent tutoring systems and significantly advance the field of intelligent education.

cs.CL↗

arXiv2021

Ultralow complexity long short-term memory network for fiber nonlinearity mitigation in coherent optical communication systems

Fiber Kerr nonlinearity is a fundamental limitation to the achievable capacity of long-distance optical fiber communication. Digital back-propagation (DBP) is a primary methodology to mitigate both linear and nonlinear impairments by solving the inverse-propagating nonlinear Schrödinger equation (NLSE), which requires detailed link information. Recently, the paradigms based on neural network (NN) were proposed to mitigate nonlinear transmission impairments in optical communication systems. However, almost all neural network-based equalization schemes yield high computation complexity, which prevents the practical implementation in commercial transmission systems. In this paper, we propose a center-oriented long short-term memory network (Co-LSTM) incorporating a simplified mode with a recycling mechanism in the equalization operation, which can mitigate fiber nonlinearity in coherent optical communication systems with ultralow complexity. To validate the proposed methodology, we carry out an experiment of ten-channel wavelength division multiplexing (WDM) transmission with 64 Gbaud polarization-division-multiplexed 16-ary quadrature amplitude modulation (16-QAM) signals. Co-LSTM and DBP achieve a comparable performance of nonlinear mitigation. However, the complexity of Co-LSTM with a simplified mode is almost independent of the transmission distance, which is much lower than that of the DBP. The proposed Co-LSTM methodology presents an attractive approach for low complexity nonlinearity mitigation with neural networks.

eess.SP↗

arXiv2018

Mean Field Games in Nudge Systems for Societal Networks

We consider the general problem of resource sharing in societal networks, consisting of interconnected communication, transportation, energy and other networks important to the functioning of society. Participants in such network need to take decisions daily, both on the quantity of resources to use as well as the periods of usage. With this in mind, we discuss the problem of incentivizing users to behave in such a way that society as a whole benefits. In order to perceive societal level impact, such incentives may take the form of rewarding users with lottery tickets based on good behavior, and periodically conducting a lottery to translate these tickets into real rewards. We will pose the user decision problem as a mean field game (MFG), and the incentives question as one of trying to select a good mean field equilibrium (MFE). In such a framework, each agent (a participant in the societal network) takes a decision based on an assumed distribution of actions of his/her competitors, and the incentives provided by the social planner. The system is said to be at MFE if the agent's action is a sample drawn from the assumed distribution. We will show the existence of such an MFE under different settings, and also illustrate how to choose an attractive equilibrium using as an example demand-response in energy networks.

cs.GT↗

arXiv2017

Scenario-based Economic Dispatch with Uncertain Demand Response

This paper introduces a new computational framework to account for uncertainties in day-ahead electricity market clearing process in the presence of demand response providers. A central challenge when dealing with many demand response providers is the uncertainty of its realization. In this paper, a new economic dispatch framework that is based on the recent theoretical development of the scenario approach is introduced. By removing samples from a finite uncertainty set, this approach improves dispatch performance while guaranteeing a quantifiable risk level with respect to the probability of violating the constraints. The theoretical bound on the level of risk is shown to be a function of the number of scenarios removed. This is appealing to the system operator for the following reasons: (1) the improvement of performance comes at the cost of a quantifiable level of violation probability in the constraints; (2) the violation upper bound does not depend on the probability distribution assumption of the uncertainty in demand response. Numerical simulations on (1) 3-bus and (2) IEEE 14-bus system (3) IEEE 118-bus system suggest that this approach could be a promising alternative in future electricity markets with multiple demand response providers.

eess.SP↗