Halo: Estimation and Reduction of Hallucinations in Open-Source Weak Large Language Models

Large Language Models (LLMs) have revolutionized Natural Language Processing (NLP). Although convenient for research and practical applications, open-source LLMs with fewer parameters often suffer from severe hallucinations compared to their larger counterparts. This paper focuses on measuring and reducing hallucinations in BLOOM 7B, a representative of such weaker open-source LLMs that are publicly available for research and commercial applications. We introduce HaloCheck, a lightweight BlackBox knowledge-free framework designed to quantify the severity of hallucinations in LLMs. Additionally, we explore techniques like knowledge injection and teacher-student approaches to alleviate hallucinations in low-parameter LLMs. Our experiments effectively demonstrate the reduction of hallucinations in challenging domains for these LLMs

Boosting Multi-Modal E-commerce Attribute Value Extraction via Unified Learning Scheme and Dynamic Range Minimization

With the prosperity of e-commerce industry, various modalities, e.g., vision and language, are utilized to describe product items. It is an enormous challenge to understand such diversified data, especially via extracting the attribute-value pairs in text sequences with the aid of helpful image regions. Although a series of previous works have been dedicated to this task, there remain seldomly investigated obstacles that hinder further improvements: 1) Parameters from up-stream single-modal pretraining are inadequately applied, without proper jointly fine-tuning in a down-stream multi-modal task. 2) To select descriptive parts of images, a simple late fusion is widely applied, regardless of priori knowledge that language-related information should be encoded into a common linguistic embedding space by stronger encoders. 3) Due to diversity across products, their attribute sets tend to vary greatly, but current approaches predict with an unnecessary maximal range and lead to more potential false positives. To address these issues, we propose in this paper a novel approach to boost multi-modal e-commerce attribute value extraction via unified learning scheme and dynamic range minimization: 1) Firstly, a unified scheme is designed to jointly train a multi-modal task with pretrained single-modal parameters. 2) Secondly, a text-guided information range minimization method is proposed to adaptively encode descriptive parts of each modality into an identical space with a powerful pretrained linguistic model. 3) Moreover, a prototype-guided attribute range minimization method is proposed to first determine the proper attribute set of the current product, and then select prototypes to guide the prediction of the chosen attributes. Experiments on the popular multi-modal e-commerce benchmarks show that our approach achieves superior performance over the other state-of-the-art techniques

Robot manipulator self-identification for surrounding obstacle detection

Obstacle detection plays an important role for robot collision avoidance and motion planning. This paper focuses on the study of the collision prediction of a dual-arm robot based on a 3D point cloud. Firstly, a self-identification method is presented based on the over-segmentation approach and the forward kinematic model of the robot. Secondly, a simplified 3D model of the robot is generated using the segmented point cloud. Finally, a collision prediction algorithm is proposed to estimate the collision parameters in real-time. Experimental studies using the KinectⓇ sensor and the BaxterⓇ robot have been performed to demonstrate the performance of the proposed algorithm

A High-Spin Rate Measurement Method for Projectiles Using a Magnetoresistive Sensor Based on Time-Frequency Domain Analysis

Traditional artillery guidance can significantly improve the attack accuracy and overall combat efficiency of projectiles, which makes it more adaptable to the information warfare of the future. Obviously, the accurate measurement of artillery spin rate, which has long been regarded as a daunting task, is the basis of precise guidance and control. Magnetoresistive (MR) sensors can be applied to spin rate measurement, especially in the high-spin and high-g projectile launch environment. In this paper, based on the theory of a MR sensor measuring spin rate, the mathematical relationship model between the frequency of MR sensor output and projectile spin rate was established through a fundamental derivation. By analyzing the characteristics of MR sensor output whose frequency varies with time, this paper proposed the Chirp z-Transform (CZT) time-frequency (TF) domain analysis method based on the rolling window of a Blackman window function (BCZT) which can accurately extract the projectile spin rate. To put it into practice, BCZT was applied to measure the spin rate of 155 mm artillery projectile. After extracting the spin rate, the impact that launch rotational angular velocity and aspect angle have on the extraction accuracy of the spin rate was analyzed. Simulation results show that the BCZT TF domain analysis method can effectively and accurately measure the projectile spin rate, especially in a high-spin and high-g projectile launch environment

Robust sliding mode control for uncertain discrete-time systems with time delay

Robust sliding mode control is designed for a class of uncertain systems with time delay in discrete time. The uncertainties include both mismatched parametric uncertainties in the state model and the matched external disturbance. After deriving sufficient conditions for the existence of linear sliding surfaces based on linear matrix inequality (LMI), robust reaching motion control is presented. A simulation study shows the effectiveness of the control scheme