As Large Language Models (LLMs) demonstrate increasingly strong human-like capabilities, the need to align them with human values has become significant. Recent advanced techniques, such as prompt learning and reinforcement learning, are being employed to bring LLMs closer to aligning with human values. While these techniques address broad ethical and helpfulness concerns, they rarely consider simulating individualized human values. To bridge this gap, we propose SimVBG, a framework that simulates individual values based on individual backstories that reflect their past experience and demographic information. SimVBG transforms structured data on an individual to a backstory and utilizes a multi-module architecture inspired by the Cognitive–Affective Personality System to simulate individual value based on the backstories. We test SimVBG on a self-constructed benchmark derived from the World Values Survey and show that SimVBG improves top-1 accuracy by more than 10% over the retrieval-augmented generation method. Further analysis shows that performance increases as additional interaction user history becomes available, indicating that the model can refine its persona over time. Code, dataset, and complete experimental results are available at https://github.com/bangdedadi/SimVBG.

Reconstructing visual images from brain signals is a rapidly evolving research with promising applications in brain-computer interfaces, cognitive neuroscience, and assistive technologies. While visual reconstruction based on functional Magnetic Resonance Imaging (fMRI) has previously achieved notable success, this paper explores cost-effective brain signals, i.e., electroencephalography (EEG) and magnetoencephalography (MEG). These signals are less precise than fMRI, which presents greater challenges for reconstruction. To address this problem, we propose BReAD (Brain Image Reconstruction with Retrieval-Augmented Diffusion), a novel framework that combines EEG/MEG signals with retrieval-augmented diffusion models to improve image reconstruction quality. BReAD utilizes the semantics decoded from brain signals for (1) retrieving semantic priors from a large-scale image database and (2) serving as a conditional constraint during the diffusion process. Extensive experiments demonstrate that BReAD significantly outperforms existing approaches in both qualitative and quantitative evaluations, paving the way for more robust and practical brain-to-image reconstruction systems. Our codes are available at https://github.com/Promise-Z5Q2SQ/BReAD.

Abstract How to collect relevance judgment has long been an important problem in Information Retrieval (IR). A popular method is to collect relevance judgment in a point‐wise manner, in which assessors examine and give an absolute relevance score for each item independently of the others. As an alternative, pair‐wise relevance judgment, also named preference judgment, allows an assessor to compare two items side‐by‐side and express their preference for one over the other. Previous work has explored the differences between these two paradigms of relevance judgments from many different aspects. Most of these works are conducted through explicit/implicit feedback. However, few works investigate the underlying neurological mechanisms of the two paradigms. In this paper, we conduct a lab study to investigate and compare point‐wise and pair‐wise relevance judgment in image search scenarios. We study the neurological mechanisms of the two paradigms through an event‐related potential (ERP) analysis of the users' brain signals while viewing images during a search process. We have obtained several observations, such as search engine users tend to pay more attention to preferred items in the point‐wise paradigm but unpreferred items in the pair‐wise paradigm. Furthermore, we test the adoption of brain signals as implicit feedback for predicting pair‐wise relevance judgment, highlighting the feasibility of leveraging brain signals to understand users' relevance judgments.

Abstract Improving the accuracy and disentanglement of attribute translation, and maintaining the consistency of face identity have been hot topics in face attribute translation. Recent approaches employ attention mechanisms to enable attribute translation in facial images. However, due to the lack of accuracy in the extraction of style code, the attention mechanism alone is not precise enough for the translation of attributes. To tackle this, we introduce a style translator module, which partitions the style code into attribute‐related and unrelated components, enhancing latent space disentanglement for more accurate attribute manipulation. Additionally, many current methods use per‐pixel loss functions to preserve face identity. However, this can sacrifice crucial high‐level features and textures in the target image. To address this limitation, we propose a multiple‐perceptual reconstruction loss to better maintain image fidelity. Extensive qualitative and quantitative experiments in this article demonstrate significant improvements over state‐of‐the‐art methods, validating the effectiveness of our approach.

While search technologies have evolved to be robust and ubiquitous, the fundamental interaction paradigm has remained relatively stable for decades. With the maturity of the Brain-Machine Interface(BMI), we build an efficient and effective communication system between human beings and search engines based on electroencephalogram (EEG) signals, called Brain Machine Search Interface (BMSI)system. The BMSI system provides functions including query reformulation and search result interaction. In our system, users can perform search tasks without having to use the mouse and keyboard. Therefore, it is useful for application scenarios in which hand-based interactions are infeasible, e.g, for users with severe neuromuscular disorders. Besides, based on brain signals decoding, our system can provide abundant and valuable user-side context information (e.g., real-time satisfaction feedback, extensive context information, and a clearer description of information needs) to the search engine, which is hard to capture in the previous paradigm. In our implementation, the system can decode user satisfaction from brain signals in real-time during the interaction process and re-rank the search results list based on user satisfaction feedback.The demo video is available at http://www.thuir.cn/group/YQLiu/videos/BMSISystem.html