Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization

Warning: this paper contains model outputs exhibiting offensiveness and biases. Recently pre-trained language models (PLMs) have prospered in various natural language generation (NLG) tasks due to their ability to generate fairly fluent text. Nevertheless, these models are observed to capture and reproduce harmful contents in training corpora, typically toxic language and social biases, raising severe moral issues. Prior works on ethical NLG tackle detoxifying and debiasing separately, which is problematic since we find debiased models still exhibit toxicity while detoxified ones even exacerbate biases. To address such a challenge, we propose the first unified framework of detoxifying and debiasing called UDDIA, which jointly formalizes these two problems as rectifying the output space. We theoretically interpret our framework as learning a text distribution mixing weighted attributes. Besides, UDDIA conducts adaptive optimization of only a few parameters during decoding based on a parameter-efficient tuning schema without any training data. This leads to minimal generation quality loss and improved rectification performance with acceptable computational cost. Experimental results demonstrate that compared to several strong baselines, UDDIA achieves debiasing and detoxifying simultaneously and better balances efficiency and effectiveness, taking a further step towards practical ethical NLG.Comment: Work in Progress. Preprin

Denevil: Towards Deciphering and Navigating the Ethical Values of Large Language Models via Instruction Learning

Large Language Models (LLMs) have made unprecedented breakthroughs, yet their increasing integration into everyday life might raise societal risks due to generated unethical content. Despite extensive study on specific issues like bias, the intrinsic values of LLMs remain largely unexplored from a moral philosophy perspective. This work delves into ethical values utilizing Moral Foundation Theory. Moving beyond conventional discriminative evaluations with poor reliability, we propose DeNEVIL, a novel prompt generation algorithm tailored to dynamically exploit LLMs' value vulnerabilities and elicit the violation of ethics in a generative manner, revealing their underlying value inclinations. On such a basis, we construct MoralPrompt, a high-quality dataset comprising 2,397 prompts covering 500+ value principles, and then benchmark the intrinsic values across a spectrum of LLMs. We discovered that most models are essentially misaligned, necessitating further ethical value alignment. In response, we develop VILMO, an in-context alignment method that substantially enhances the value compliance of LLM outputs by learning to generate appropriate value instructions, outperforming existing competitors. Our methods are suitable for black-box and open-source models, offering a promising initial step in studying the ethical values of LLMs

Improved V?shaped interior permanent magnet rotor topology with inward?extended bridges for reduced torque ripple

Interior permanent magnet synchronous machines (IPMSMs) with V-shaped permanent magnet (PM) rotors are widely used as traction motors in electric vehicles because of their high torque density and high efficiency. However, the V-shape IPMSMs have the disadvantages of inevitable torque ripple due to the non-sinusoidal air-gap flux density distribution and the utilisation of the reluctance torque. In this study, with the aim of improving the torque ripple characteristics, a modified V-shaped IPMSM rotor configuration with bridges extended inwards towards the pole centre is proposed to generate a more sinusoidal air-gap flux density waveform. The proposed topology, referred to as ‘Type C’ within this study, is compared with baseline rotor configuration references, namely ‘Type A’ which is a conventional V-shaped PM rotor, as well as ‘Type B’ which is a related configuration with a mechanically non-uniform air gap. The analysis results show that the rotor ‘Type C’ exhibits significant advantages in terms of reducing cogging torque, torque ripple and radial force, without incurring additional air-gap friction losses. Finally, a prototype of the IPMSM with the proposed rotor configuration is manufactured and tested, verifying the predicted benefits experimentally

Delay-dependent exponential stability criteria for stochastic systems with polytopic-type uncertainties

This paper considers the problem of delay-dependent exponential stability in mean square for continuous-time linear stochastic systems with polytopic-type uncertainties and time-varying delay. Based on linear matrix inequalities (LMIs), applying the descriptor model transformation and introducing free weighting matrices, a new type of Lyapunov-Krasovskii functional is constructed and some new delay-dependent and delay-independent exponential stability criteria are respectively obtained. The results include the delay-independent/rate-dependent and delay-dependent/rate-independent exponential stability criteria. The new criteria are less conservative than existing ones. Numerical examples demonstrate the new criteria are effective and are an improvement over existing ones

Angle dependent field-driven reorientation transitions in uniaxial antiferromagnet MnBi2_2Te4_4 single crystal

MnBi$_2$Te$_4$, a two-dimensional magnetic topological insulator with a uniaxial antiferromagnetic structure, is an ideal platform to realize quantum anomalous Hall effect. However, the strength of magnetic interactions is not clear yet. We performed systematic studies on the magnetization and angle dependent magnetotransport of MnBi$_2$Te$_4$ single crystal. The results show that the direction of the magnetic field has significant effects on the critical field values and magnetic structure of this compound, which leads to different magnetotransport behaviors. The field-driven reorientation transitions can be utilized to estimate the AFM interlayer exchange interaction coupling and uniaxial magnetic anisotropy D. The obtained Hamiltonian can well explain the experimental data by Monte Carlo simulations. Our comprehensive studies on the field-driven magnetic transitions phenomenon in MnBi$_2$Te$_4$ provide a general approach for other topological systems with antiferromagnetism.Comment: 6 figure