Unveiling A Core Linguistic Region in Large Language Models

Brain localization, which describes the association between specific regions of the brain and their corresponding functions, is widely accepted in the field of cognitive science as an objective fact. Today's large language models (LLMs) possess human-level linguistic competence and can execute complex tasks requiring abstract knowledge and reasoning. To deeply understand the inherent mechanisms of intelligence emergence in LLMs, this paper conducts an analogical research using brain localization as a prototype. We have discovered a core region in LLMs that corresponds to linguistic competence, accounting for approximately 1% of the total model parameters. This core region exhibits significant dimension dependency, and perturbations to even a single parameter on specific dimensions can lead to a loss of linguistic competence. Furthermore, we observe that an improvement in linguistic competence does not necessarily accompany an elevation in the model's knowledge level, which might imply the existence of regions of domain knowledge that are dissociated from the linguistic region. Overall, exploring the LLMs' functional regions provides insights into the foundation of their intelligence. In the future, we will continue to investigate knowledge regions within LLMs and the interactions between them.Comment: Work on progres

Compound Attitudes, Customer Engagement and eWOM: An Empirical Study on WeChat

The purpose of this study is to build a framework to explain the relationships among compound attitudes (i.e., affective attitudes, cognitive attitude), customer engagement and eWOM(electronic word of mouth) behaviors in the context of WeChat. Based on the relevant theories and practices of compound attitudes, customer engagement, and eWOM, we proposed a conceptual model. This research enhanced the understanding of compound attitudes, customer engagement, and eWOM. These finding will not only help to better understand the mechanism of eWOM communication in the context of social media, but also help the Integrated Marketing Communication (IMC) marketers to develop effective social media marketing strategies and build strong consumer – brand (product) relationships

A novel zero-forcing transmit data scheme for multiuser MIMO broadcast channel

International audienceThe sum rate maximization in the multiuser MIMO broadcast channel is investigated in this paper. Because of higher computational complexity of non-linear Dirty Paper Coding (DPC), linear approaches are often used instead. Even through transmit and receive beamforming optimizations are still nonconvex problems in general case. For the scenario where the number of transmit antennas is larger than the users' antennas in sum, substantial techniques such as BD or channel inverse have been proposed. In this paper, we consider the scenario where the sum number of receive antennas is much more than the number of the transmit antennas under the zero-forcing constraint. It is shown that the optimal data stream allocation needs exhaustive search over all possibilities, and the complexity is significantly high. We propose a greedy transmit data allocation scheme that allocates one data stream at each step, and the corresponding transmit and receive beamforming vectors are designed to optimize the sum rate. Simulation results show that the proposed method outperforms the methods in the literature

A iterative power allocation methode in multiuser MIMO broadcast channel

International audienceIn this paper, sum rate optimization of multiuser multiple-input multiple-output downlink (MUMIMO) communication systems is investigated with perfect channel state information (CSI) at the transmitter. Taking power efficiency into account, the proposed iterative power allocation algorithm aims to optimize the global throughput of the entire system instead of maximizing the minimum weighted individual signal-to-interference-plus-noise ratio (SINR). Simulations show that the sum rate of the proposed method achieves the sum capacity of the MU-MIMO broadcast channel, especially in low signal-to-noise ratio (SNR) region

Efficient power allocation strategy in multiuser MIMO broadcast channels

International audienceIn this paper, sum rate optimization of multiuser multiple-input multiple-output broadcast (MU-MIMO) communication systems with perfect channel state information (CSI) at the base station is investigated. Since power allocation is a signomial optimization problem in the presence of multiuser interference (MUI), it is not a convex problem in general, several optimal solutions proposed in the literature have exponential computational complexity, which is hard to implement for practice. We propose an iterative water-filling algorithm that takes advantage of the classical simple water-filling principle. The proposed algorithm reduces significantly the computational complexity compared with the methods in the literature only with a negligible performance degradation. In addition, the generalized eigenvalue technique for beamforming design is utilized in this paper for minimizing MUI. And the number of users and the number of antennas of each user can be arbitrary. Simulations show that the sum rate of the proposed method is close to the sum capacity of the MU-MIMO broadcast channel, especially in low signal-to-noise ratio (SNR) region

Efficient power allocation strategy in multiuser MIMO broadcast channels

International audienceIn this paper, sum rate optimization of multiuser multiple-input multiple-output broadcast (MU-MIMO) communication systems with perfect channel state information (CSI) at the base station is investigated. Since power allocation is a signomial optimization problem in the presence of multiuser interference (MUI), it is not a convex problem in general, several optimal solutions proposed in the literature have exponential computational complexity, which is hard to implement for practice. We propose an iterative water-filling algorithm that takes advantage of the classical simple water-filling principle. The proposed algorithm reduces significantly the computational complexity compared with the methods in the literature only with a negligible performance degradation. In addition, the generalized eigenvalue technique for beamforming design is utilized in this paper for minimizing MUI. And the number of users and the number of antennas of each user can be arbitrary. Simulations show that the sum rate of the proposed method is close to the sum capacity of the MU-MIMO broadcast channel, especially in low signal-to-noise ratio (SNR) region

Zero-forcing DPC beamforming design for multiuser MIMO broadcast channels

International audienceThe sum rate maximization in multiuser MIMO broadcast channels is investigated in this paper. We first propose an approach under a total powerconstraint. Compared with the most related methods in the literature, the proposed method can be easily adapted to a more realistic per-antenna powerconstraint. Since the power of each antenna is limited individually by the linearity of its power amplifier in practice, the per-antenna power constraintis more practical. Secondly, we propose a novel beamforming method under the per-antenna power constraint, a significant sum rate improvement isachieved compared with the methods in the literature. Moreover, the proposed method works even if the number of total receive antennas is largerthan that of transmit antennas