ValueSim: Generating Backstories to Model Individual Value Systems

As Large Language Models (LLMs) continue to exhibit increasingly human-like capabilities, aligning them with human values has become critically important. Contemporary advanced techniques, such as prompt learning and reinforcement learning, are being deployed to better align LLMs with human values. However, while these approaches address broad ethical considerations and helpfulness, they rarely focus on simulating individualized human value systems. To address this gap, we present ValueSim, a framework that simulates individual values through the generation of personal backstories reflecting past experiences and demographic information. ValueSim converts structured individual data into narrative backstories and employs a multi-module architecture inspired by the Cognitive-Affective Personality System to simulate individual values based on these narratives. Testing ValueSim on a self-constructed benchmark derived from the World Values Survey demonstrates an improvement in top-1 accuracy by over 10% compared to retrieval-augmented generation methods. Further analysis reveals that performance enhances as additional user interaction history becomes available, indicating the model's ability to refine its persona simulation capabilities over time.

EEG-ImageNet: An Electroencephalogram Dataset and Benchmarks with Image Visual Stimuli of Multi-Granularity Labels

Identifying and reconstructing what we see from brain activity gives us a special insight into investigating how the biological visual system represents the world. While recent efforts have achieved high-performance image classification and high-quality image reconstruction from brain signals collected by Functional Magnetic Resonance Imaging (fMRI) or magnetoencephalogram (MEG), the expensiveness and bulkiness of these devices make relevant applications difficult to generalize to practical applications. On the other hand, Electroencephalography (EEG), despite its advantages of ease of use, cost-efficiency, high temporal resolution, and non-invasive nature, has not been fully explored in relevant studies due to the lack of comprehensive datasets. To address this gap, we introduce EEG-ImageNet, a novel EEG dataset comprising recordings from 16 subjects exposed to 4000 images selected from the ImageNet dataset. EEG-ImageNet consists of 5 times EEG-image pairs larger than existing similar EEG benchmarks. EEG-ImageNet is collected with image stimuli of multi-granularity labels, i.e., 40 images with coarse-grained labels and 40 with fine-grained labels. Based on it, we establish benchmarks for object classification and image reconstruction. Experiments with several commonly used models show that the best models can achieve object classification with accuracy around 60% and image reconstruction with two-way identification around 64%. These results demonstrate the dataset's potential to advance EEG-based visual brain-computer interfaces, understand the visual perception of biological systems, and provide potential applications in improving machine visual models.

Web Search via an Efficient and Effective Brain-Machine Interface

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, 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/datasets/BMSISystem.mp4.